Mental health of nurses and doctors in Ukraine during wartime: a nationwide cross-sectional survey

library(tidyverse)

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library(gtsummary)
library(tidyr)
library(sjlabelled)

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library(here)

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library(patchwork)
library(ggh4x)

Prepare information

ds <- readRDS("data/ukr_data.rds")  

cb <- read.csv("ext/data_mapping.csv")

# source("src/viridis_noyellow.R")

variable_labels <- # labels for survey data variables
  
  readxl::read_xlsx("ext/var_labels.xlsx")

source("src/functions.R")

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Ukraine conflict singularities

ua_reg_conflict <- 
  readxl::read_excel("ext/ukr_conflict_reg.xlsx") |>
  mutate(occupation = str_to_title(occupation, locale = "en"),
         combat = str_to_title(combat, locale = "en")) |> 
  mutate(occupation = factor(occupation, levels = c("Yes",
                                                       "Partial",
                                                       "No")),
         combat = factor(combat, levels = c("Yes", "No")),
         loc_3 = as_factor(str_trim(loc_3)),
         loc_en = as_factor(loc_en)
  )

# Add to ds

ds_ua <- 
  ds |>  
  left_join(ua_reg_conflict, by = "loc_3") |> 
  filter(occupation != "Yes")

ds_ua <- 
  ds_ua |> 
  mutate(conflict = if_else(
    occupation == "No" & combat == "No",
    "No conflict zone",
    "Conflict zone"
    ),
    conflict = factor(conflict,
                      levels = c("No conflict zone", 
                                 "Conflict zone"), 
                      ordered = FALSE),
    situation = case_when(
      occupation == "Partial" ~ "Partially occupied",
      combat == "Yes" ~ "Actively in combat",
      TRUE ~ "No conflict area"
    ),
    situation = factor(situation, levels = c("No conflict area",
                                             "Partially occupied",
                                             "Actively in combat"),
                       ordered = FALSE))

Manage variables

ds_ua <- 
  
  ds_ua |> 
  mutate(work_5_rec = fct_collapse(
    work_5,
    "Emergencies" = c("Emergency care outside hospital (e.g., ambulance)",
                      "Emergency department"),
    "In-hospital care" = "Inpatient care (hospital services)",
    "Outpatient care" = c("Primary care (outpatient services)", 
                          "Secondary care (outpatient services)",
                          "Other")),
    work_5_rec = factor(work_5_rec, levels = c("Outpatient care",
                                               "In-hospital care",
                                               "Emergencies"),
                        ordered = FALSE)
    )


ds_ua <- 
  
  ds_ua |> 
  mutate(work_3_rec = fct_collapse(
    work_3,
    "5 years or less" = c("Less than 1 year", "1-3 years", "4-5 years"),
    "More than 10 years" = c("11-20 years", "21-30 years", "31-40 years",
                             "More than 40 years")
  ))


ds_ua <- 
  
  ds_ua |> 
  mutate(scdm_1_rec = fct_collapse(
    scdm_1,
    "20-30" = c("20-25", "26-30"),
    "31-45" = c("31-35", "36-40", "41-45"),
    "46-55" = c("46-50", "51-55"),
    "Over 55" = c("56-60", "61-65", "66-70", "Over 70")
  ))

# EXP Sociodemographic

exp_scdm <- c("scdm_4", "scdm_5", "scdm_6")
exp_scdm_dic <- paste0(exp_scdm, "_dic")

# EXP Occupational

exp_work <- c("work_5", "work_16")
exp_work_dic <- paste0(exp_work, "_dic")

# EXP Violence

exp_vio <- c("work_30", "work_31", "work_32", "work_33")
exp_vio_dic <- paste0(exp_vio, "_dic")

# EXP protective factors

exp_supp <- c("work_21", "work_22", "wb_wami_1", "wb_wami_2")
exp_supp_dic <- paste0(exp_supp, "_dic")

# add to dataset

ds_ua <- 
  
  ds_ua |> 
  
  # recode marital status
  
  mutate(
    scdm_4_dic = fct_collapse(scdm_4,
                              "No partner" = c("Divorced",
                                               "Separated",
                                               "Single",
                                               "Widowed"),
                              "Has a partner" = c("Married",
                                                  "Registered partnership")),
    scdm_4_dic = factor(scdm_4_dic, 
                        levels = c("No partner", "Has a partner"),
                        ordered = FALSE)
  ) |> 
  
  # recode children in household
  
  mutate(
    scdm_5_dic = case_when(
      str_detect(scdm_5, "with children") ~ "Has children",
      str_detect(scdm_5, "without children") ~ "No children",
    TRUE ~ NA),
    scdm_5_dic = factor(scdm_5_dic, 
                        levels = c("Has children", "No children"),
                        ordered = FALSE)
  ) |> 
  
  # recode making ends meet
  
  mutate(
    scdm_6_dic = case_when(
      str_detect(scdm_6, "difficulty") ~ "Difficultly",
      str_detect(scdm_6, "easily") ~ "Easily",
    TRUE ~ NA),
    scdm_6_dic = factor(scdm_6_dic, 
                        levels = c("Difficultly", "Easily"),
                        ordered = FALSE)
  ) |> 
  
  # create dichotomic for contract type
  
  mutate(work_16_dic = fct_recode(work_16,
                                  "Yes" = "Temporary contract",
                                  "No" = "Fixed contract"),
         work_16_dic = factor(work_16_dic,
                              levels = c("Yes", 
                                         "No"),
                              ordered = FALSE)
  ) |> 
  
  # recode violence
  
   mutate(
    across(
      all_of(exp_vio),
      ~ factor(
        fct_collapse(.x,
                     Yes = c("Yes, daily", 
                             "Yes, weekly", 
                             "Yes, monthly", 
                             "Yes, a few times"),
                     No = "No"
        ),
        levels = c("No", "Yes"), ordered = FALSE
      ),
      .names = "{.col}_dic"
    )
  ) |>
  
  # Recode protocols, drop "Do not know", ensure factor levels
 
   mutate(
     work_29_dic = fct_recode(work_29, 
                              NULL = "Do not know")
    ) |> 

  # relevel protocols

  mutate(
    work_29_dic = fct_relevel(work_29_dic, "No")
    ) |> 
  
  # recode support 
  
  mutate(
    across(
      all_of(c("work_21", "work_22")),
      ~ factor(
        fct_collapse(.x,
                     Yes = c("Always", "Often"),
                     No = c("Sometimes", "Seldom", "Never / Hardly ever")
        ),
        levels = c("Yes", "No"), ordered = FALSE
      ),
      .names = "{col}_dic"
    )
  ) |> 
  
  # recode meaningful career
  
  mutate(
    wb_wami_1_dic = fct_collapse(wb_wami_1,
                                 "Yes" = c("Absolutely true", "Mostly true"),
                                 "No" = c("Absolutely untrue", 
                                          "Mostly untrue"),
                                 NULL = "Neither true nor untrue"),
    wb_wami_1_dic = factor(wb_wami_1_dic, levels = c("Yes", "No"))
  ) |> 
  
  # recode purpose
  
  mutate(
    wb_wami_2_dic = fct_collapse(wb_wami_2,
                                 "Yes" = c("Absolutely true", "Mostly true"),
                                 "No" = c("Absolutely untrue", 
                                          "Mostly untrue"),
                                 NULL = "Neither true nor untrue"),
    wb_wami_2_dic = factor(wb_wami_2_dic, levels = c("Yes", "No"))
  )




outcomes <- c("phq_co", "gad_co", "suic_idea")


# Poisson models need numeric values

ds_ua <- 
  
  ds_ua |> 
  mutate(
    across(
      all_of(outcomes),
      ~ case_when(
        . == "Yes" ~ 1,
        . == "No" ~ 0,
        TRUE ~ NA_real_
      )
    )
  )

exposures <- c("work_21_dic", "work_22_dic",
               "work_30_dic", "work_31_dic", 
               "work_32_dic", "work_33_dic",
               "wb_wami_1_dic", "wb_wami_2_dic")


# Remove observations
ds_ua <- 
  ds_ua |> 
  filter(scdm_1 != "Less than 20 years old") |> 
  mutate(scdm_1 = fct_drop(scdm_1),
         loc_3 = fct_drop(loc_3)) |> 
  mutate(
    across(
      where(is.ordered),
      ~ factor(., ordered = FALSE)
    )
  ) |> 
  mutate(
    across(
      all_of(exposures),
      ~ factor(.x, levels = c("No", "Yes"))
    )
  )



# labels


for (i in 1:nrow(variable_labels)) {
  uuid <- variable_labels$Question_uuid[i]
  label <-  variable_labels$Label[i]
  ds_ua[[uuid]] <- sjlabelled::set_label(ds_ua[[uuid]], label)
}

rm(variable_labels,
   i,
   uuid,
   label)

outcome_order <- c(
  "Depression",
  "Anxiety",
  "Suicide thoughts"
  )

exposure_order <- c(
  "Bullying",
  "Sexual Harassment",
  "Violent threats",
  "Physical violence",
  "Colleague support",
  "Support from superiors",
  "Meaning in work",
  "Purpose in work"
)

adjustment_order <- c(
  "Crude",
  "Partially adjusted",
  "Fully adjusted"
  )

# define covariates and outcomes

outcomes_list <- c("phq_co", "gad_co", "suic_idea")

# for model 2 in each
covars_structural <- c("conflict", "scdm_1_rec", "scdm_2_rec")

# specific

covars_peers <- c("work_5_rec", "work_3_rec")

covars_harbul <- c("work_5_rec", "work_1", "work_3_rec")
  
covars_viol <- c("work_5_rec")

covars_meansup <- c("work_5_rec", "scdm_4_dic", "scdm_5_dic", "work_3_rec")

# assign covariates to each exposure
exposure_map <- list(
  
  "work_21_dic" = covars_peers,
  "work_22_dic" = covars_peers,
  
  "work_30_dic" = covars_harbul,
  "work_33_dic" = covars_harbul,
  
  "work_31_dic" = covars_viol,
  "work_32_dic" = covars_viol,
  
  "wb_wami_1_dic" = covars_meansup,
  "wb_wami_2_dic" = covars_meansup
)

Unweighted tables

scdm_vars <- c("scdm_1_rec", "scdm_4_dic", "scdm_5_dic", "work_1",
               "work_3_rec", "work_5_rec", "conflict")

scdm_tbl <- 
  ds_ua |> 
  select(all_of(scdm_vars), work_2, scdm_2_rec) |> 
  tbl_strata(
    strata = "work_2",
    ~ tbl_summary(
      .,
      by = "scdm_2_rec",
      missing_text = "Missing values"
    ) |> 
      add_overall()
  ) |> 
  as_gt() |> 
  tab_footnote(footnote = "Conflict zones are defined by Proximity to combat areas and occupied territories.")

## 30 missing rows in the "scdm_2_rec" column have been removed.
## 38 missing rows in the "scdm_2_rec" column have been removed.

scdm_tbl

Characteristic	Doctor			Nurse
	Overall N = 10,1581	Female N = 7,6621	Male N = 2,4961	Overall N = 13,5791	Female N = 12,7441	Male N = 8351
Age groups (Recoded)
20-30	1,694 (17%)	1,214 (16%)	480 (19%)	2,142 (16%)	1,866 (15%)	276 (33%)
31-45	3,330 (33%)	2,567 (34%)	763 (31%)	5,181 (38%)	4,931 (39%)	250 (30%)
46-55	2,063 (20%)	1,561 (20%)	502 (20%)	3,999 (29%)	3,793 (30%)	206 (25%)
Over 55	3,071 (30%)	2,320 (30%)	751 (30%)	2,257 (17%)	2,154 (17%)	103 (12%)
Relationship status
No partner	3,507 (35%)	2,843 (37%)	664 (27%)	4,541 (33%)	4,233 (33%)	308 (37%)
Has a partner	6,639 (65%)	4,811 (63%)	1,828 (73%)	9,027 (67%)	8,500 (67%)	527 (63%)
Missing values	12	8	4	11	11	0
Having children in the household
Has children	6,449 (64%)	4,940 (65%)	1,509 (61%)	9,982 (74%)	9,497 (75%)	485 (58%)
No children	3,692 (36%)	2,711 (35%)	981 (39%)	3,574 (26%)	3,224 (25%)	350 (42%)
Missing values	17	11	6	23	23	0
Post-graduate training	1,026 (10%)	738 (9.7%)	288 (12%)	1,140 (8.4%)	1,026 (8.1%)	114 (14%)
Missing values	26	21	5	81	79	2
Length of service (Recoded)
5 years or less	1,836 (18%)	1,371 (18%)	465 (19%)	1,281 (9.6%)	1,087 (8.7%)	194 (24%)
6-10 years	1,209 (12%)	885 (12%)	324 (13%)	1,431 (11%)	1,310 (10%)	121 (15%)
More than 10 years	6,943 (70%)	5,272 (70%)	1,671 (68%)	10,613 (80%)	10,117 (81%)	496 (61%)
Missing values	170	134	36	254	230	24
Healthcare setting (Recoded)
Outpatient care	6,000 (60%)	4,935 (66%)	1,065 (43%)	5,703 (43%)	5,571 (45%)	132 (16%)
In-hospital care	3,117 (31%)	2,054 (27%)	1,063 (43%)	5,030 (38%)	4,920 (40%)	110 (14%)
Emergencies	835 (8.4%)	508 (6.8%)	327 (13%)	2,417 (18%)	1,856 (15%)	561 (70%)
Missing values	206	165	41	429	397	32
Proximity to combat areas and occupied territories
No conflict zone	8,440 (83%)	6,304 (82%)	2,136 (86%)	11,031 (81%)	10,345 (81%)	686 (82%)
Conflict zone	1,718 (17%)	1,358 (18%)	360 (14%)	2,548 (19%)	2,399 (19%)	149 (18%)
Conflict zones are defined by Proximity to combat areas and occupied territories.
1 n (%)

gtsave(scdm_tbl, "out/tables/scdm_tbl_unw.docx")

# We calculate the prevalences with confidence intervals
# Good practice because it gives certain information about the sample

# Run for your outcomes
tbl_depression_u <- 
  generate_unweighted_table_out(ds_ua, "phq_co",
                                "Probable Depression")

gtsave(tbl_depression_u, "out/tables/dep_by_scdm_unweighted.docx")

tbl_anxiety_u <- 
  generate_unweighted_table_out(ds_ua, "gad_co", "Probable Anxiety")

gtsave(tbl_anxiety_u, "out/tables/anx_by_scdm_unweighted.docx")

tbl_suic_u <- 
  generate_unweighted_table_out(ds_ua, "suic_idea",
                                "Passive suicide thoughts")

gtsave(tbl_suic_u, "out/tables/suic_by_scdm_unweighted.docx")

# Display
tbl_depression_u

Probable Depression (Unweighted)
	Doctor			Nurse
	Male (N = 2,129)	Female (N = 6,431)	Overall (N = 8,578)	Male (N = 677)	Female (N = 10,313)	Overall (N = 11,017)
Overall
Overall	33.8% (31.8-35.9)	35.5% (34.4-36.7)	35.1% (34.1-36.2)	27.5% (24.2-31.0)	22.4% (21.6-23.3)	22.7% (21.9-23.5)
Age groups (Recoded)
20-30	46.1% (41.2-51.1)	48.3% (45.1-51.4)	47.6% (45.0-50.3)	34.8% (28.7-41.5)	32.7% (30.3-35.1)	32.9% (30.7-35.2)
31-45	45.8% (41.9-49.6)	42.3% (40.2-44.5)	43.2% (41.3-45.0)	29.9% (23.8-36.8)	23.8% (22.5-25.2)	24.1% (22.8-25.5)
46-55	27.4% (23.3-31.9)	36.2% (33.6-38.8)	34.1% (31.9-36.4)	23.7% (17.6-30.9)	22.3% (20.8-23.8)	22.3% (20.9-23.8)
Over 55	17.8% (15.0-21.1)	21.0% (19.3-22.9)	20.2% (18.7-21.9)	8.8% (3.9-17.7)	10.7% (9.3-12.3)	10.6% (9.2-12.1)
Relationship status
No partner	44.8% (40.6-49.0)	36.2% (34.3-38.2)	37.9% (36.2-39.7)	31.5% (25.9-37.7)	25.1% (23.7-26.7)	25.6% (24.2-27.1)
Has a partner	30.0% (27.7-32.3)	35.1% (33.7-36.6)	33.7% (32.5-34.9)	25.1% (21.1-29.5)	21.1% (20.2-22.1)	21.3% (20.4-22.2)
Having children in the household
Has children	29.8% (27.3-32.4)	33.6% (32.2-35.1)	32.7% (31.5-34.0)	25.6% (21.5-30.3)	21.3% (20.4-22.2)	21.4% (20.6-22.4)
No children	39.8% (36.5-43.3)	39.0% (37.0-41.1)	39.3% (37.6-41.0)	30.0% (24.8-35.8)	25.9% (24.3-27.7)	26.4% (24.8-28.0)
Post-graduate training
No	35.0% (32.8-37.2)	35.8% (34.6-37.1)	35.7% (34.6-36.7)	27.0% (23.5-30.8)	22.6% (21.7-23.4)	22.8% (22.0-23.6)
Yes	24.3% (19.1-30.5)	32.5% (28.6-36.6)	30.0% (26.9-33.4)	31.0% (21.8-42.0)	20.7% (17.8-23.8)	21.7% (18.9-24.7)
Length of service (Recoded)
5 years or less	42.3% (37.4-47.4)	45.2% (42.2-48.1)	44.4% (41.9-47.0)	34.0% (26.7-42.1)	32.3% (29.1-35.6)	32.5% (29.7-35.6)
6-10 years	51.1% (45.1-57.0)	47.7% (44.1-51.3)	48.5% (45.4-51.5)	32.3% (23.5-42.6)	27.0% (24.4-29.8)	27.4% (24.9-30.1)
More than 10 years	27.9% (25.6-30.3)	31.1% (29.8-32.5)	30.4% (29.2-31.6)	24.1% (20.2-28.5)	20.9% (20.0-21.8)	21.0% (20.2-21.9)
Healthcare setting (Recoded)
Outpatient care	32.9% (29.9-36.1)	34.9% (33.4-36.3)	34.5% (33.2-35.9)	25.9% (18.3-35.2)	22.8% (21.6-24.0)	22.8% (21.6-24.0)
In-hospital care	34.8% (31.7-38.0)	36.1% (33.9-38.4)	35.7% (33.9-37.5)	25.8% (17.4-36.4)	20.9% (19.7-22.2)	21.0% (19.8-22.3)
Emergencies	33.7% (28.3-39.5)	40.5% (35.9-45.3)	37.7% (34.2-41.3)	28.2% (24.3-32.5)	25.9% (23.7-28.2)	26.5% (24.5-28.5)
Proximity to combat areas and occupied territories
No conflict zone	33.8% (31.6-36.0)	35.5% (34.2-36.8)	35.1% (34.0-36.2)	26.3% (22.7-30.2)	21.9% (21.0-22.8)	22.1% (21.3-23.0)
Conflict zone	34.0% (28.7-39.7)	35.9% (33.1-38.8)	35.5% (33.1-38.1)	32.5% (24.6-41.5)	24.9% (23.0-26.9)	25.4% (23.5-27.3)
Unweighted Prevalence (95% CI).

tbl_anxiety_u

Probable Anxiety (Unweighted)
	Doctor			Nurse
	Male (N = 2,134)	Female (N = 6,438)	Overall (N = 8,590)	Male (N = 680)	Female (N = 10,323)	Overall (N = 11,030)
Overall
Overall	21.7% (20.0-23.5)	25.7% (24.6-26.8)	24.7% (23.8-25.7)	16.3% (13.7-19.4)	16.6% (15.9-17.3)	16.6% (15.9-17.3)
Age groups (Recoded)
20-30	29.3% (25.0-34.1)	36.5% (33.6-39.6)	34.6% (32.1-37.1)	20.1% (15.2-26.1)	24.2% (22.0-26.5)	23.7% (21.7-25.8)
31-45	29.2% (25.8-32.9)	30.9% (28.9-32.9)	30.6% (28.9-32.3)	18.0% (13.2-24.2)	16.6% (15.4-17.8)	16.7% (15.6-17.8)
46-55	17.7% (14.3-21.7)	25.7% (23.4-28.1)	23.7% (21.8-25.8)	13.0% (8.5-19.3)	16.9% (15.6-18.3)	16.7% (15.5-18.1)
Over 55	11.6% (9.3-14.4)	14.4% (12.9-16.1)	13.7% (12.4-15.1)	8.5% (3.8-17.3)	9.5% (8.2-11.0)	9.4% (8.1-10.9)
Relationship status
No partner	28.9% (25.2-32.8)	25.5% (23.8-27.3)	26.2% (24.6-27.8)	19.6% (15.0-25.1)	18.4% (17.2-19.8)	18.6% (17.3-19.9)
Has a partner	19.2% (17.3-21.3)	25.8% (24.4-27.2)	24.0% (22.9-25.1)	14.4% (11.2-18.1)	15.7% (14.8-16.6)	15.6% (14.8-16.4)
Having children in the household
Has children	19.3% (17.2-21.5)	23.9% (22.7-25.3)	22.9% (21.8-24.0)	14.1% (10.9-18.0)	15.8% (15.0-16.6)	15.7% (14.9-16.5)
No children	25.3% (22.4-28.4)	28.9% (27.1-30.8)	28.0% (26.4-29.6)	19.4% (15.1-24.6)	19.0% (17.5-20.6)	19.1% (17.7-20.6)
Post-graduate training
No	22.1% (20.2-24.0)	26.0% (24.9-27.1)	25.1% (24.1-26.1)	16.6% (13.7-19.9)	16.7% (16.0-17.5)	16.7% (16.0-17.5)
Yes	18.2% (13.6-23.9)	22.4% (19.0-26.2)	21.2% (18.4-24.2)	14.8% (8.4-24.3)	15.1% (12.6-17.9)	14.9% (12.6-17.6)
Length of service (Recoded)
5 years or less	26.4% (22.2-31.1)	33.8% (31.0-36.6)	32.0% (29.6-34.4)	19.0% (13.3-26.3)	24.5% (21.6-27.5)	23.6% (21.0-26.4)
6-10 years	32.0% (26.7-37.9)	35.4% (32.0-38.9)	34.4% (31.6-37.4)	19.0% (12.1-28.3)	20.1% (17.8-22.7)	20.0% (17.8-22.4)
More than 10 years	18.4% (16.5-20.5)	22.1% (20.9-23.3)	21.2% (20.2-22.3)	14.8% (11.7-18.6)	15.4% (14.6-16.2)	15.4% (14.6-16.1)
Healthcare setting (Recoded)
Outpatient care	22.1% (19.5-25.0)	25.9% (24.6-27.3)	25.3% (24.1-26.5)	16.1% (10.1-24.5)	17.4% (16.4-18.6)	17.4% (16.4-18.5)
In-hospital care	22.2% (19.6-25.0)	24.8% (22.8-26.9)	23.9% (22.3-25.6)	15.7% (9.2-25.3)	14.6% (13.6-15.8)	14.6% (13.6-15.8)
Emergencies	19.2% (14.9-24.3)	27.2% (23.2-31.7)	24.2% (21.2-27.5)	16.5% (13.4-20.2)	19.5% (17.5-21.6)	18.8% (17.1-20.6)
Proximity to combat areas and occupied territories
No conflict zone	21.8% (20.0-23.8)	25.4% (24.2-26.6)	24.5% (23.5-25.5)	15.5% (12.7-18.9)	15.9% (15.1-16.7)	15.9% (15.1-16.7)
Conflict zone	20.8% (16.5-25.9)	27.2% (24.7-29.9)	25.9% (23.7-28.2)	19.8% (13.5-28.1)	19.6% (17.8-21.4)	19.6% (18.0-21.4)
Unweighted Prevalence (95% CI).

tbl_suic_u

Passive suicide thoughts (Unweighted)
	Doctor			Nurse
	Male (N = 2,137)	Female (N = 6,460)	Overall (N = 8,615)	Male (N = 682)	Female (N = 10,367)	Overall (N = 11,076)
Overall
Overall	15.2% (13.7-16.8)	11.4% (10.6-12.2)	12.3% (11.7-13.1)	12.6% (10.3-15.4)	8.0% (7.5-8.5)	8.3% (7.8-8.8)
Age groups (Recoded)
20-30	22.5% (18.6-26.9)	18.4% (16.1-20.9)	19.6% (17.6-21.8)	17.3% (12.8-23.1)	14.1% (12.4-16.0)	14.5% (12.9-16.3)
31-45	20.8% (17.8-24.1)	13.1% (11.7-14.7)	15.0% (13.7-16.4)	11.7% (7.8-17.1)	7.7% (6.9-8.5)	7.9% (7.1-8.8)
46-55	11.4% (8.7-14.9)	11.1% (9.5-13.0)	11.2% (9.8-12.8)	10.7% (6.6-16.5)	7.8% (6.9-8.8)	8.0% (7.1-8.9)
Over 55	7.3% (5.5-9.7)	6.0% (5.0-7.2)	6.3% (5.4-7.3)	6.0% (2.2-14.1)	3.8% (3.0-4.9)	3.9% (3.1-4.9)
Relationship status
No partner	21.7% (18.4-25.4)	13.5% (12.1-14.9)	15.0% (13.7-16.4)	16.0% (11.9-21.2)	10.5% (9.5-11.6)	10.9% (9.9-11.9)
Has a partner	13.0% (11.4-14.7)	10.2% (9.3-11.1)	11.0% (10.2-11.8)	10.6% (7.9-14.0)	6.8% (6.2-7.4)	7.0% (6.4-7.6)
Having children in the household
Has children	12.8% (11.1-14.8)	10.1% (9.2-11.1)	10.8% (10.0-11.6)	10.3% (7.6-13.9)	7.0% (6.5-7.6)	7.2% (6.6-7.8)
No children	18.9% (16.3-21.7)	13.6% (12.3-15.1)	15.1% (13.8-16.4)	15.8% (11.9-20.7)	10.8% (9.7-12.1)	11.3% (10.2-12.6)
Post-graduate training
No	15.1% (13.6-16.8)	11.2% (10.4-12.1)	12.2% (11.5-13.0)	12.3% (9.8-15.3)	7.9% (7.4-8.4)	8.1% (7.6-8.7)
Yes	15.9% (11.6-21.4)	12.8% (10.2-15.9)	13.6% (11.4-16.3)	14.8% (8.4-24.3)	9.4% (7.4-11.8)	9.9% (8.0-12.2)
Length of service (Recoded)
5 years or less	21.9% (18.0-26.3)	16.0% (13.9-18.2)	17.5% (15.7-19.5)	20.8% (14.8-28.2)	13.0% (10.8-15.4)	14.2% (12.1-16.5)
6-10 years	24.6% (19.8-30.1)	15.4% (13.0-18.2)	17.9% (15.6-20.3)	13.0% (7.4-21.6)	12.8% (10.9-15.0)	12.8% (11.0-14.9)
More than 10 years	11.6% (10.0-13.4)	9.6% (8.7-10.5)	10.1% (9.3-10.9)	9.6% (7.1-12.9)	6.9% (6.3-7.4)	7.0% (6.5-7.6)
Healthcare setting (Recoded)
Outpatient care	15.6% (13.3-18.1)	11.0% (10.1-12.0)	11.9% (11.0-12.8)	9.7% (5.2-17.1)	7.9% (7.1-8.7)	7.9% (7.2-8.7)
In-hospital care	15.6% (13.3-18.1)	11.1% (9.7-12.7)	12.7% (11.5-14.0)	10.1% (5.0-18.8)	7.2% (6.4-8.0)	7.2% (6.5-8.1)
Emergencies	13.0% (9.5-17.5)	15.7% (12.5-19.5)	14.6% (12.2-17.4)	13.8% (10.9-17.3)	10.5% (9.0-12.1)	11.3% (10.0-12.8)
Proximity to combat areas and occupied territories
No conflict zone	15.8% (14.2-17.6)	11.2% (10.4-12.1)	12.4% (11.7-13.2)	12.2% (9.7-15.3)	7.8% (7.2-8.4)	8.0% (7.5-8.6)
Conflict zone	11.6% (8.3-15.8)	12.1% (10.3-14.2)	12.0% (10.4-13.8)	14.3% (8.9-21.9)	8.8% (7.6-10.2)	9.2% (8.1-10.6)
Unweighted Prevalence (95% CI).

# Run function for your outcomes
tbl_harassment_u <- generate_unweighted_table_exp(ds_ua, "work_30_dic", 
                                              "Sexual harassment")
gtsave(tbl_harassment_u, "out/tables/harass_by_scdm_unweighted.docx")

tbl_threats_u <- generate_unweighted_table_exp(ds_ua, "work_31_dic", 
                                           "Violent threats")
gtsave(tbl_threats_u, "out/tables/threats_by_scdm_unweighted.docx")

tbl_violence_u <- generate_unweighted_table_exp(ds_ua, "work_32_dic", 
                                            "Physical violence")
gtsave(tbl_violence_u, "out/tables/violence_by_scdm_unweighted.docx")

tbl_bullying_u <- generate_unweighted_table_exp(ds_ua, "work_33_dic", 
                                            "Bullying")
gtsave(tbl_bullying_u, "out/tables/bullying_by_scdm_unweighted.docx")

tbl_colleagues_u <- generate_unweighted_table_exp(ds_ua, "work_21_dic",
                                              "Colleague support")
gtsave(tbl_colleagues_u, "out/tables/col_supp_by_scdm_unweighted.docx")

tbl_superiors_u <- generate_unweighted_table_exp(ds_ua,"work_22_dic", 
                                         "Support from superiors")
gtsave(tbl_superiors_u, "out/tables/sup_supp_by_scdm_unweighted.docx")
 
tbl_meaning_u <- generate_unweighted_table_exp(ds_ua, "wb_wami_1_dic", 
                                           "Meaning in work")
gtsave(tbl_meaning_u, "out/tables/meaning_by_scdm_unweighted.docx")

tbl_purpose_u <- generate_unweighted_table_exp(ds_ua, "wb_wami_2_dic", 
                                           "Purpose in work")
gtsave(tbl_purpose_u, "out/tables/purpose_by_scdm_unweighted.docx")

# Visualize

tbl_harassment_u

Sexual harassment (Unweighted)
	Doctor			Nurse
	Male (N = 2,226)	Female (N = 6,705)	Overall (N = 8,951)	Male (N = 716)	Female (N = 10,765)	Overall (N = 11,510)
Overall
Overall	8.2% (7.1-9.5)	9.8% (9.1-10.5)	9.4% (8.8-10.1)	8.8% (6.9-11.2)	6.8% (6.3-7.3)	6.9% (6.4-7.4)
Age groups (Recoded)
20-30	14.1% (11.0-17.8)	22.3% (19.9-25.0)	20.0% (18.1-22.2)	12.2% (8.4-17.2)	16.9% (15.1-18.9)	16.3% (14.6-18.1)
31-45	10.9% (8.7-13.5)	13.1% (11.7-14.6)	12.6% (11.4-13.9)	7.9% (4.8-12.6)	7.2% (6.5-8.1)	7.3% (6.5-8.1)
46-55	6.2% (4.3-9.0)	6.1% (4.9-7.5)	6.2% (5.2-7.5)	6.3% (3.3-11.3)	4.3% (3.7-5.1)	4.4% (3.8-5.2)
Over 55	3.0% (1.9-4.7)	2.0% (1.5-2.8)	2.3% (1.8-3.0)	6.8% (2.8-14.8)	1.3% (0.8-2.0)	1.5% (1.1-2.2)
Relationship status
No partner	14.5% (11.8-17.7)	12.4% (11.1-13.7)	12.9% (11.7-14.1)	11.9% (8.4-16.6)	9.3% (8.3-10.3)	9.4% (8.5-10.4)
Has a partner	6.0% (4.9-7.3)	8.3% (7.5-9.1)	7.6% (7.0-8.4)	6.9% (4.8-9.8)	5.6% (5.1-6.1)	5.7% (5.2-6.2)
Having children in the household
Has children	6.0% (4.8-7.4)	7.7% (6.9-8.6)	7.3% (6.7-8.1)	7.0% (4.8-10.0)	5.9% (5.4-6.4)	6.0% (5.5-6.5)
No children	11.7% (9.7-14.1)	13.5% (12.2-15.0)	13.1% (12.0-14.3)	11.3% (8.0-15.5)	9.4% (8.4-10.6)	9.6% (8.6-10.7)
Post-graduate training
No	7.8% (6.7-9.1)	9.4% (8.7-10.2)	9.1% (8.4-9.7)	7.9% (5.9-10.4)	6.6% (6.1-7.1)	6.6% (6.2-7.1)
Yes	11.7% (8.1-16.6)	13.8% (11.1-17.0)	13.2% (11.0-15.8)	15.1% (8.8-24.3)	9.4% (7.5-11.8)	10.1% (8.2-12.3)
Length of service (Recoded)
5 years or less	14.9% (11.7-18.8)	19.9% (17.7-22.3)	18.7% (16.8-20.7)	11.1% (6.9-17.2)	18.5% (16.0-21.2)	17.3% (15.1-19.8)
6-10 years	11.2% (8.0-15.5)	16.1% (13.6-18.9)	14.7% (12.7-17.0)	13.0% (7.5-21.1)	11.1% (9.3-13.1)	11.2% (9.5-13.1)
More than 10 years	5.8% (4.7-7.2)	6.1% (5.5-6.9)	6.1% (5.5-6.8)	7.0% (4.9-9.9)	5.0% (4.6-5.5)	5.1% (4.7-5.6)
Healthcare setting (Recoded)
Outpatient care	6.6% (5.1-8.4)	9.0% (8.1-9.9)	8.6% (7.9-9.4)	11.1% (6.3-18.6)	5.2% (4.6-5.9)	5.3% (4.8-6.0)
In-hospital care	9.4% (7.7-11.5)	10.5% (9.1-12.0)	10.2% (9.1-11.4)	11.1% (5.7-19.9)	7.1% (6.3-7.9)	7.1% (6.4-8.0)
Emergencies	9.6% (6.6-13.6)	14.8% (11.7-18.5)	12.8% (10.6-15.5)	7.7% (5.6-10.4)	10.8% (9.3-12.4)	10.0% (8.8-11.4)
Proximity to combat areas and occupied territories
No conflict zone	8.5% (7.3-9.8)	9.9% (9.2-10.7)	9.6% (9.0-10.3)	8.4% (6.3-11.0)	6.9% (6.4-7.5)	7.0% (6.5-7.5)
Conflict zone	6.7% (4.3-10.1)	9.1% (7.5-10.9)	8.6% (7.2-10.2)	10.7% (6.2-17.6)	6.2% (5.2-7.4)	6.5% (5.5-7.7)
Unweighted Prevalence (95% CI)

tbl_threats_u

Violent threats (Unweighted)
	Doctor			Nurse
	Male (N = 2,218)	Female (N = 6,689)	Overall (N = 8,927)	Male (N = 715)	Female (N = 10,744)	Overall (N = 11,488)
Overall
Overall	27.2% (25.4-29.1)	17.6% (16.7-18.6)	20.1% (19.2-20.9)	29.0% (25.7-32.5)	10.2% (9.7-10.8)	11.4% (10.8-12.0)
Age groups (Recoded)
20-30	35.0% (30.5-39.7)	23.7% (21.2-26.4)	27.0% (24.8-29.4)	31.8% (26.0-38.2)	15.6% (13.8-17.5)	17.8% (16.1-19.7)
31-45	35.2% (31.7-39.0)	20.8% (19.2-22.6)	24.2% (22.7-25.8)	28.8% (23.0-35.5)	11.1% (10.2-12.1)	12.0% (11.0-13.0)
46-55	24.1% (20.3-28.4)	17.3% (15.3-19.4)	18.9% (17.2-20.8)	28.7% (22.3-36.2)	8.8% (7.9-9.8)	9.8% (8.9-10.9)
Over 55	16.1% (13.4-19.1)	11.2% (9.9-12.7)	12.4% (11.2-13.7)	21.8% (14.0-32.2)	6.2% (5.1-7.4)	6.8% (5.8-8.1)
Relationship status
No partner	31.4% (27.7-35.4)	18.8% (17.3-20.4)	21.3% (19.9-22.8)	32.0% (26.5-38.0)	12.1% (11.1-13.3)	13.5% (12.5-14.7)
Has a partner	25.8% (23.7-28.0)	16.9% (15.8-18.1)	19.4% (18.4-20.5)	27.1% (23.1-31.6)	9.3% (8.7-10.0)	10.3% (9.7-11.1)
Having children in the household
Has children	26.9% (24.5-29.3)	17.0% (15.9-18.1)	19.3% (18.3-20.4)	29.1% (24.8-33.7)	9.7% (9.0-10.3)	10.6% (10.0-11.3)
No children	27.7% (24.8-30.9)	18.8% (17.3-20.5)	21.2% (19.9-22.7)	28.8% (23.8-34.3)	11.9% (10.8-13.3)	13.7% (12.5-15.0)
Post-graduate training
No	27.0% (25.1-29.1)	17.9% (16.9-18.8)	20.1% (19.3-21.0)	29.1% (25.6-32.9)	10.1% (9.5-10.7)	11.2% (10.6-11.8)
Yes	28.7% (23.2-34.9)	15.4% (12.6-18.7)	19.4% (16.8-22.3)	28.0% (19.4-38.4)	12.1% (9.9-14.6)	13.8% (11.6-16.3)
Length of service (Recoded)
5 years or less	32.0% (27.6-36.8)	20.2% (18.0-22.6)	23.3% (21.3-25.5)	28.8% (22.2-36.5)	13.8% (11.6-16.3)	16.1% (14.0-18.5)
6-10 years	38.9% (33.3-44.8)	24.1% (21.2-27.2)	28.0% (25.4-30.8)	36.4% (27.5-46.4)	13.9% (12.0-16.1)	15.9% (13.9-18.1)
More than 10 years	23.7% (21.6-26.0)	15.9% (14.9-17.0)	17.8% (16.9-18.8)	27.3% (23.3-31.8)	9.4% (8.8-10.0)	10.3% (9.7-10.9)
Healthcare setting (Recoded)
Outpatient care	24.4% (21.8-27.3)	17.1% (16.0-18.3)	18.4% (17.4-19.5)	18.8% (12.4-27.3)	7.6% (6.9-8.4)	7.9% (7.2-8.7)
In-hospital care	26.7% (23.9-29.6)	16.1% (14.4-17.9)	19.8% (18.3-21.3)	16.9% (10.0-26.6)	9.5% (8.6-10.4)	9.6% (8.7-10.5)
Emergencies	37.7% (32.3-43.5)	28.9% (24.8-33.4)	32.5% (29.2-36.0)	33.3% (29.2-37.6)	20.4% (18.4-22.5)	23.5% (21.7-25.4)
Proximity to combat areas and occupied territories
No conflict zone	27.8% (25.8-29.9)	18.0% (17.0-19.0)	20.5% (19.6-21.4)	27.9% (24.3-31.8)	10.2% (9.6-10.9)	11.3% (10.7-12.0)
Conflict zone	23.7% (19.3-28.8)	16.1% (14.0-18.3)	17.8% (15.9-19.8)	33.6% (25.7-42.4)	10.4% (9.1-11.8)	11.8% (10.5-13.3)
Unweighted Prevalence (95% CI)

tbl_violence_u

Physical violence (Unweighted)
	Doctor			Nurse
	Male (N = 2,218)	Female (N = 6,677)	Overall (N = 8,915)	Male (N = 713)	Female (N = 10,733)	Overall (N = 11,474)
Overall
Overall	5.5% (4.6-6.6)	2.5% (2.2-2.9)	3.3% (2.9-3.7)	10.9% (8.8-13.5)	3.1% (2.8-3.5)	3.6% (3.3-4.0)
Age groups (Recoded)
20-30	9.6% (7.1-12.9)	4.9% (3.7-6.5)	6.3% (5.2-7.7)	10.1% (6.7-14.9)	6.1% (5.0-7.4)	6.6% (5.5-7.9)
31-45	5.8% (4.3-7.9)	3.2% (2.5-4.0)	3.9% (3.2-4.6)	10.2% (6.7-15.3)	3.3% (2.8-3.9)	3.6% (3.1-4.2)
46-55	4.0% (2.5-6.4)	1.8% (1.2-2.7)	2.3% (1.7-3.2)	10.3% (6.4-16.1)	2.3% (1.9-2.9)	2.7% (2.2-3.4)
Over 55	3.5% (2.3-5.3)	1.0% (0.6-1.6)	1.6% (1.2-2.2)	16.1% (9.4-25.9)	1.6% (1.1-2.3)	2.2% (1.6-3.0)
Relationship status
No partner	8.9% (6.8-11.6)	3.0% (2.4-3.8)	4.2% (3.5-5.0)	10.1% (6.9-14.5)	4.3% (3.7-5.1)	4.7% (4.1-5.5)
Has a partner	4.3% (3.4-5.4)	2.2% (1.8-2.7)	2.8% (2.4-3.3)	11.4% (8.7-14.8)	2.5% (2.2-2.9)	3.0% (2.7-3.5)
Having children in the household
Has children	4.5% (3.5-5.8)	2.2% (1.8-2.7)	2.8% (2.4-3.2)	10.9% (8.2-14.4)	2.9% (2.5-3.3)	3.3% (2.9-3.7)
No children	7.0% (5.5-9.0)	3.2% (2.5-4.0)	4.2% (3.6-5.0)	11.0% (7.8-15.2)	3.8% (3.1-4.6)	4.5% (3.8-5.3)
Post-graduate training
No	5.1% (4.2-6.2)	2.3% (2.0-2.7)	3.0% (2.7-3.4)	10.8% (8.5-13.6)	3.0% (2.6-3.3)	3.4% (3.1-3.8)
Yes	8.6% (5.5-13.0)	4.7% (3.2-6.9)	6.0% (4.5-7.9)	11.8% (6.3-20.6)	5.0% (3.6-6.8)	5.7% (4.3-7.5)
Length of service (Recoded)
5 years or less	8.9% (6.4-12.2)	4.8% (3.7-6.2)	5.9% (4.8-7.2)	8.6% (5.0-14.3)	6.3% (4.9-8.2)	6.7% (5.3-8.4)
6-10 years	7.2% (4.6-10.9)	3.0% (2.0-4.5)	4.1% (3.0-5.5)	10.4% (5.5-18.2)	4.3% (3.3-5.8)	4.9% (3.8-6.3)
More than 10 years	4.2% (3.3-5.4)	1.9% (1.5-2.3)	2.5% (2.1-2.9)	12.0% (9.2-15.4)	2.6% (2.3-3.0)	3.1% (2.7-3.5)
Healthcare setting (Recoded)
Outpatient care	4.2% (3.1-5.7)	1.9% (1.5-2.4)	2.3% (2.0-2.8)	6.8% (3.2-13.4)	1.6% (1.3-2.0)	1.7% (1.4-2.1)
In-hospital care	4.3% (3.1-5.8)	2.0% (1.5-2.8)	2.8% (2.3-3.5)	5.6% (2.1-13.2)	3.6% (3.1-4.2)	3.7% (3.1-4.3)
Emergencies	13.6% (10.0-18.1)	10.6% (8.0-13.9)	11.9% (9.7-14.5)	12.6% (9.9-15.9)	6.4% (5.2-7.7)	7.9% (6.8-9.1)
Proximity to combat areas and occupied territories
No conflict zone	5.7% (4.7-6.9)	2.6% (2.2-3.0)	3.4% (3.0-3.8)	10.3% (8.0-13.1)	3.2% (2.8-3.6)	3.6% (3.2-4.0)
Conflict zone	4.3% (2.5-7.3)	2.3% (1.6-3.4)	2.8% (2.1-3.8)	13.8% (8.6-21.3)	2.9% (2.2-3.8)	3.6% (2.8-4.5)
Unweighted Prevalence (95% CI)

tbl_bullying_u

Bullying (Unweighted)
	Doctor			Nurse
	Male (N = 2,218)	Female (N = 6,670)	Overall (N = 8,907)	Male (N = 710)	Female (N = 10,717)	Overall (N = 11,455)
Overall
Overall	24.5% (22.7-26.3)	27.4% (26.3-28.5)	26.7% (25.8-27.6)	22.0% (19.0-25.2)	18.1% (17.4-18.8)	18.3% (17.6-19.0)
Age groups (Recoded)
20-30	34.5% (30.0-39.3)	40.5% (37.5-43.5)	38.8% (36.3-41.3)	25.4% (20.1-31.6)	26.7% (24.5-29.0)	26.5% (24.5-28.7)
31-45	31.4% (28.0-35.1)	30.7% (28.8-32.6)	30.9% (29.2-32.6)	22.9% (17.6-29.2)	18.9% (17.8-20.2)	19.1% (17.9-20.3)
46-55	20.3% (16.7-24.4)	25.5% (23.2-27.9)	24.2% (22.3-26.3)	18.5% (13.2-25.3)	16.2% (15.0-17.5)	16.3% (15.1-17.6)
Over 55	13.6% (11.2-16.5)	18.1% (16.5-19.9)	17.0% (15.6-18.5)	17.2% (10.3-27.2)	11.9% (10.5-13.5)	12.2% (10.8-13.8)
Relationship status
No partner	33.5% (29.7-37.5)	29.3% (27.6-31.2)	30.2% (28.5-31.8)	24.4% (19.5-30.1)	21.6% (20.2-23.0)	21.7% (20.4-23.1)
Has a partner	21.3% (19.4-23.4)	26.2% (24.9-27.6)	24.9% (23.8-26.0)	20.5% (16.9-24.6)	16.4% (15.5-17.3)	16.6% (15.8-17.5)
Having children in the household
Has children	20.7% (18.6-23.0)	25.7% (24.4-27.0)	24.5% (23.4-25.6)	18.9% (15.3-23.1)	17.1% (16.3-18.0)	17.2% (16.4-18.0)
No children	30.3% (27.3-33.5)	30.4% (28.6-32.3)	30.4% (28.9-32.1)	26.2% (21.4-31.6)	21.0% (19.5-22.6)	21.5% (20.0-23.0)
Post-graduate training
No	23.8% (21.9-25.7)	27.3% (26.2-28.5)	26.5% (25.5-27.5)	21.3% (18.1-24.8)	17.9% (17.2-18.7)	18.1% (17.4-18.9)
Yes	29.9% (24.3-36.2)	27.8% (24.2-31.8)	28.4% (25.4-31.7)	26.9% (18.5-37.2)	20.1% (17.3-23.1)	20.7% (18.1-23.6)
Length of service (Recoded)
5 years or less	31.1% (26.7-35.8)	36.4% (33.7-39.2)	35.1% (32.8-37.5)	22.8% (16.8-30.2)	26.7% (23.9-29.8)	26.1% (23.5-28.9)
6-10 years	36.5% (31.1-42.3)	37.2% (33.8-40.6)	36.9% (34.1-39.9)	30.5% (22.1-40.3)	21.8% (19.4-24.4)	22.5% (20.2-25.0)
More than 10 years	20.3% (18.3-22.4)	23.4% (22.2-24.6)	22.7% (21.6-23.7)	19.7% (16.2-23.8)	16.7% (15.9-17.5)	16.8% (16.1-17.6)
Healthcare setting (Recoded)
Outpatient care	21.6% (19.1-24.4)	25.5% (24.2-26.8)	24.9% (23.7-26.0)	19.7% (13.1-28.2)	16.1% (15.1-17.1)	16.1% (15.1-17.2)
In-hospital care	26.6% (23.8-29.5)	30.9% (28.8-33.1)	29.4% (27.7-31.1)	21.3% (13.7-31.6)	19.3% (18.1-20.5)	19.3% (18.2-20.5)
Emergencies	27.2% (22.3-32.6)	31.6% (27.4-36.2)	30.0% (26.8-33.5)	22.5% (18.9-26.4)	21.0% (19.1-23.2)	21.4% (19.6-23.2)
Proximity to combat areas and occupied territories
No conflict zone	25.5% (23.6-27.5)	27.8% (26.7-29.0)	27.3% (26.3-28.3)	21.6% (18.3-25.2)	18.2% (17.4-19.1)	18.4% (17.6-19.2)
Conflict zone	18.5% (14.5-23.3)	25.1% (22.7-27.7)	23.7% (21.6-26.0)	23.8% (17.0-32.3)	17.4% (15.8-19.2)	17.7% (16.2-19.5)
Unweighted Prevalence (95% CI)

tbl_colleagues_u

Colleague support (Unweighted)
	Doctor			Nurse
	Male (N = 2,259)	Female (N = 6,804)	Overall (N = 9,085)	Male (N = 729)	Female (N = 11,007)	Overall (N = 11,765)
Overall
Overall	58.7% (56.7-60.8)	59.8% (58.7-61.0)	59.5% (58.5-60.5)	54.6% (50.9-58.2)	58.3% (57.3-59.2)	58.1% (57.2-58.9)
Age groups (Recoded)
20-30	54.9% (50.1-59.7)	60.0% (57.0-63.0)	58.5% (56.0-61.0)	57.0% (50.5-63.2)	58.7% (56.2-61.1)	58.5% (56.2-60.8)
31-45	53.0% (49.2-56.8)	54.8% (52.8-56.9)	54.3% (52.5-56.1)	48.8% (42.0-55.7)	54.5% (53.0-56.0)	54.2% (52.8-55.7)
46-55	60.7% (56.0-65.2)	60.3% (57.7-62.9)	60.4% (58.1-62.6)	56.5% (48.9-63.9)	59.5% (57.9-61.2)	59.4% (57.7-61.0)
Over 55	65.8% (62.1-69.4)	65.0% (62.8-67.0)	65.0% (63.2-66.8)	58.2% (47.4-68.3)	64.4% (62.2-66.6)	64.1% (61.9-66.3)
Relationship status
No partner	54.1% (50.0-58.2)	60.0% (58.1-61.9)	58.8% (57.0-60.5)	54.9% (48.8-60.9)	58.0% (56.3-59.6)	57.7% (56.2-59.3)
Has a partner	60.4% (58.0-62.8)	59.7% (58.2-61.2)	59.8% (58.6-61.1)	54.4% (49.7-59.0)	58.4% (57.3-59.5)	58.2% (57.1-59.3)
Having children in the household
Has children	60.0% (57.3-62.6)	60.0% (58.5-61.5)	59.9% (58.6-61.2)	54.0% (49.1-58.8)	57.9% (56.8-59.0)	57.8% (56.7-58.8)
No children	56.7% (53.3-60.0)	59.5% (57.5-61.5)	58.7% (57.0-60.4)	55.4% (49.6-61.0)	59.3% (57.4-61.1)	58.9% (57.1-60.6)
Post-graduate training
No	59.2% (57.0-61.4)	60.0% (58.8-61.2)	59.7% (58.6-60.8)	54.0% (50.1-58.0)	58.7% (57.8-59.7)	58.5% (57.5-59.4)
Yes	55.0% (48.6-61.2)	58.4% (54.2-62.4)	57.2% (53.8-60.6)	57.7% (47.3-67.6)	52.2% (48.6-55.7)	52.9% (49.5-56.2)
Length of service (Recoded)
5 years or less	57.7% (52.9-62.5)	61.6% (58.8-64.3)	60.5% (58.1-62.9)	61.7% (53.8-69.0)	60.6% (57.3-63.8)	60.8% (57.8-63.7)
6-10 years	50.7% (44.9-56.5)	55.7% (52.2-59.1)	54.1% (51.1-57.1)	51.4% (41.7-61.0)	57.6% (54.7-60.5)	57.2% (54.3-59.9)
More than 10 years	60.5% (58.0-62.9)	60.1% (58.7-61.5)	60.1% (58.9-61.3)	53.0% (48.3-57.7)	58.1% (57.1-59.1)	57.9% (56.9-58.9)
Healthcare setting (Recoded)
Outpatient care	58.3% (55.1-61.4)	58.2% (56.7-59.6)	58.1% (56.8-59.4)	66.7% (57.3-74.9)	59.2% (57.8-60.5)	59.3% (58.0-60.7)
In-hospital care	59.6% (56.5-62.7)	63.1% (60.9-65.3)	61.8% (60.0-63.6)	57.0% (46.3-67.1)	57.8% (56.3-59.3)	57.8% (56.3-59.3)
Emergencies	57.6% (51.8-63.2)	62.2% (57.5-66.6)	60.2% (56.6-63.6)	51.4% (47.0-55.7)	56.9% (54.4-59.3)	55.6% (53.4-57.7)
Proximity to combat areas and occupied territories
No conflict zone	58.0% (55.7-60.2)	59.7% (58.4-61.0)	59.2% (58.1-60.3)	55.4% (51.4-59.5)	58.2% (57.2-59.3)	58.1% (57.1-59.1)
Conflict zone	63.2% (57.7-68.4)	60.5% (57.6-63.2)	61.0% (58.5-63.4)	50.8% (42.0-59.5)	58.3% (56.1-60.5)	57.9% (55.8-59.9)
Unweighted Prevalence (95% CI)

tbl_superiors_u

Support from superiors (Unweighted)
	Doctor			Nurse
	Male (N = 2,255)	Female (N = 6,793)	Overall (N = 9,070)	Male (N = 729)	Female (N = 10,974)	Overall (N = 11,732)
Overall
Overall	49.5% (47.5-51.6)	52.7% (51.5-53.9)	51.8% (50.8-52.9)	48.7% (45.0-52.4)	51.2% (50.3-52.2)	51.1% (50.2-52.0)
Age groups (Recoded)
20-30	43.8% (39.1-48.6)	47.8% (44.8-50.8)	46.5% (44.0-49.1)	44.3% (38.0-50.7)	48.9% (46.4-51.4)	48.3% (46.0-50.6)
31-45	43.5% (39.8-47.3)	49.3% (47.2-51.4)	47.9% (46.1-49.7)	46.1% (39.4-53.0)	48.3% (46.8-49.9)	48.3% (46.8-49.7)
46-55	50.6% (45.8-55.3)	51.9% (49.3-54.6)	51.5% (49.2-53.8)	53.1% (45.5-60.6)	51.8% (50.1-53.5)	51.8% (50.2-53.5)
Over 55	58.8% (55.0-62.6)	59.6% (57.4-61.8)	59.3% (57.4-61.2)	58.2% (47.4-68.3)	59.1% (56.8-61.4)	59.2% (56.9-61.4)
Relationship status
No partner	47.4% (43.3-51.5)	52.7% (50.7-54.6)	51.5% (49.7-53.3)	46.5% (40.5-52.6)	51.3% (49.6-52.9)	50.9% (49.4-52.5)
Has a partner	50.3% (47.9-52.7)	52.7% (51.2-54.2)	52.0% (50.7-53.3)	50.0% (45.4-54.6)	51.2% (50.1-52.4)	51.2% (50.1-52.3)
Having children in the household
Has children	50.4% (47.8-53.1)	53.2% (51.7-54.7)	52.4% (51.1-53.7)	50.0% (45.3-54.7)	50.7% (49.7-51.8)	50.8% (49.7-51.8)
No children	48.1% (44.7-51.4)	51.9% (49.9-53.9)	50.7% (49.0-52.4)	46.9% (41.2-52.7)	52.7% (50.8-54.6)	52.1% (50.3-53.9)
Post-graduate training
No	48.9% (46.7-51.1)	52.2% (51.0-53.5)	51.3% (50.2-52.4)	46.8% (42.8-50.7)	50.6% (49.6-51.6)	50.4% (49.5-51.3)
Yes	54.6% (48.2-60.8)	57.7% (53.6-61.7)	56.5% (53.1-59.9)	60.8% (50.4-70.4)	59.4% (55.8-62.8)	59.7% (56.4-62.9)
Length of service (Recoded)
5 years or less	46.5% (41.7-51.3)	50.7% (47.9-53.6)	49.5% (47.1-52.0)	50.3% (42.5-58.1)	52.4% (49.1-55.6)	52.0% (49.0-55.1)
6-10 years	39.6% (34.0-45.4)	49.7% (46.2-53.2)	46.9% (43.9-49.9)	39.4% (30.4-49.3)	48.4% (45.4-51.3)	47.7% (44.8-50.5)
More than 10 years	52.3% (49.8-54.8)	53.7% (52.2-55.1)	53.2% (52.0-54.5)	50.1% (45.4-54.8)	51.5% (50.4-52.5)	51.5% (50.4-52.5)
Healthcare setting (Recoded)
Outpatient care	51.5% (48.3-54.7)	53.2% (51.7-54.7)	52.8% (51.5-54.1)	60.7% (51.2-69.5)	52.8% (51.4-54.2)	53.1% (51.7-54.4)
In-hospital care	48.1% (45.0-51.3)	52.4% (50.1-54.7)	50.9% (49.0-52.7)	45.2% (34.9-55.8)	49.6% (48.1-51.1)	49.5% (48.0-51.0)
Emergencies	48.0% (42.3-53.8)	48.7% (44.0-53.4)	48.2% (44.6-51.8)	46.5% (42.2-50.9)	50.6% (48.2-53.1)	49.7% (47.5-51.8)
Proximity to combat areas and occupied territories
No conflict zone	48.6% (46.3-50.9)	52.2% (50.9-53.5)	51.2% (50.0-52.3)	50.4% (46.3-54.5)	50.7% (49.7-51.7)	50.7% (49.7-51.7)
Conflict zone	55.0% (49.5-60.5)	55.1% (52.3-58.0)	55.0% (52.5-57.6)	40.9% (32.5-49.8)	53.6% (51.4-55.8)	52.8% (50.7-54.9)
Unweighted Prevalence (95% CI)

tbl_meaning_u

Meaning in work (Unweighted)
	Doctor			Nurse
	Male (N = 1,718)	Female (N = 5,289)	Overall (N = 7,020)	Male (N = 525)	Female (N = 8,186)	Overall (N = 8,733)
Overall
Overall	81.3% (79.3-83.1)	84.5% (83.4-85.4)	83.6% (82.7-84.5)	68.6% (64.4-72.5)	80.6% (79.8-81.5)	79.9% (79.1-80.7)
Age groups (Recoded)
20-30	78.5% (73.5-82.8)	83.7% (80.8-86.2)	82.2% (79.7-84.4)	64.1% (56.4-71.2)	80.8% (78.3-83.1)	78.5% (76.2-80.8)
31-45	80.3% (76.6-83.6)	82.1% (80.2-83.8)	81.6% (79.9-83.2)	72.8% (65.0-79.4)	77.8% (76.3-79.3)	77.6% (76.1-79.0)
46-55	80.9% (76.3-84.8)	82.5% (80.0-84.7)	82.1% (80.0-84.0)	68.3% (59.3-76.1)	80.9% (79.3-82.4)	80.3% (78.7-81.8)
Over 55	84.0% (80.6-87.0)	88.6% (87.0-90.1)	87.5% (86.0-88.8)	70.4% (58.2-80.4)	86.2% (84.3-87.9)	85.4% (83.6-87.2)
Relationship status
No partner	75.6% (71.2-79.5)	84.0% (82.3-85.6)	82.4% (80.8-83.9)	70.7% (63.8-76.8)	81.7% (80.2-83.2)	80.9% (79.4-82.3)
Has a partner	83.2% (81.0-85.2)	84.8% (83.5-86.0)	84.3% (83.2-85.3)	67.3% (61.9-72.3)	80.1% (79.0-81.1)	79.4% (78.3-80.4)
Having children in the household
Has children	82.3% (79.9-84.6)	84.2% (82.9-85.4)	83.7% (82.6-84.8)	70.6% (65.2-75.5)	80.5% (79.4-81.4)	80.0% (79.0-81.0)
No children	79.6% (76.3-82.5)	85.0% (83.3-86.6)	83.6% (82.0-85.0)	65.6% (58.6-71.9)	81.2% (79.4-82.8)	79.6% (77.9-81.3)
Post-graduate training
No	81.2% (79.1-83.1)	84.6% (83.6-85.6)	83.8% (82.8-84.6)	69.8% (65.3-73.9)	80.6% (79.7-81.5)	80.0% (79.1-80.9)
Yes	82.1% (75.7-87.1)	82.8% (79.0-86.1)	82.5% (79.3-85.3)	61.3% (49.4-72.1)	81.2% (77.8-84.2)	78.8% (75.5-81.8)
Length of service (Recoded)
5 years or less	80.4% (75.5-84.5)	84.0% (81.4-86.3)	83.1% (80.8-85.1)	71.6% (62.3-79.3)	83.1% (79.9-86.0)	81.3% (78.3-84.0)
6-10 years	75.2% (68.8-80.7)	82.5% (79.2-85.3)	80.6% (77.7-83.2)	62.2% (50.1-73.0)	79.6% (76.7-82.2)	78.2% (75.3-80.8)
More than 10 years	82.7% (80.4-84.8)	84.9% (83.7-86.0)	84.3% (83.3-85.3)	69.1% (63.8-73.9)	80.6% (79.6-81.5)	80.0% (79.1-80.9)
Healthcare setting (Recoded)
Outpatient care	79.2% (76.0-82.1)	83.2% (81.9-84.4)	82.4% (81.3-83.6)	74.7% (64.3-83.0)	80.1% (78.7-81.3)	79.9% (78.6-81.2)
In-hospital care	84.6% (81.8-87.1)	87.9% (86.1-89.5)	86.8% (85.3-88.2)	62.7% (50.0-73.9)	81.2% (79.8-82.5)	80.9% (79.5-82.2)
Emergencies	76.1% (69.9-81.5)	82.6% (78.0-86.4)	79.9% (76.3-83.1)	68.0% (62.9-72.7)	80.8% (78.4-82.9)	77.7% (75.5-79.7)
Proximity to combat areas and occupied territories
No conflict zone	80.6% (78.5-82.6)	83.8% (82.6-84.9)	82.9% (81.9-83.9)	70.2% (65.5-74.5)	80.4% (79.4-81.3)	79.8% (78.8-80.7)
Conflict zone	85.1% (79.8-89.2)	87.5% (85.2-89.5)	87.0% (84.9-88.8)	62.4% (52.6-71.3)	81.8% (79.8-83.7)	80.5% (78.5-82.3)
Unweighted Prevalence (95% CI)

tbl_purpose_u

Purpose in work (Unweighted)
	Doctor			Nurse
	Male (N = 1,806)	Female (N = 5,558)	Overall (N = 7,376)	Male (N = 587)	Female (N = 8,906)	Overall (N = 9,519)
Overall
Overall	87.7% (86.0-89.1)	90.7% (89.9-91.5)	89.9% (89.2-90.6)	84.7% (81.4-87.4)	89.2% (88.5-89.8)	88.9% (88.3-89.5)
Age groups (Recoded)
20-30	87.2% (83.0-90.5)	87.8% (85.3-89.9)	87.6% (85.6-89.4)	76.6% (69.8-82.3)	89.1% (87.3-90.8)	87.5% (85.7-89.2)
31-45	84.3% (80.9-87.1)	89.5% (88.0-90.8)	88.2% (86.8-89.5)	90.3% (84.8-94.0)	87.1% (85.9-88.2)	87.2% (86.1-88.3)
46-55	89.7% (86.0-92.6)	90.5% (88.6-92.1)	90.3% (88.7-91.7)	85.6% (78.4-90.8)	90.0% (88.8-91.1)	89.8% (88.6-90.8)
Over 55	89.9% (87.1-92.2)	93.7% (92.4-94.8)	92.7% (91.5-93.7)	89.3% (79.5-95.0)	92.6% (91.1-93.8)	92.5% (91.1-93.7)
Relationship status
No partner	84.8% (81.1-87.9)	90.7% (89.3-91.9)	89.6% (88.3-90.7)	81.0% (75.0-85.9)	89.8% (88.7-90.9)	89.2% (88.1-90.3)
Has a partner	88.6% (86.7-90.2)	90.8% (89.7-91.7)	90.1% (89.2-90.9)	86.8% (82.8-90.0)	88.9% (88.1-89.7)	88.8% (88.0-89.6)
Having children in the household
Has children	88.6% (86.5-90.4)	90.9% (89.9-91.8)	90.3% (89.4-91.1)	87.4% (83.4-90.6)	89.3% (88.5-90.0)	89.2% (88.4-89.9)
No children	86.1% (83.2-88.5)	90.5% (89.1-91.7)	89.3% (88.1-90.5)	80.4% (74.6-85.2)	89.1% (87.7-90.3)	88.2% (86.9-89.5)
Post-graduate training
No	87.4% (85.6-89.0)	90.6% (89.8-91.4)	89.8% (89.1-90.5)	86.1% (82.8-88.9)	89.0% (88.3-89.7)	88.9% (88.2-89.5)
Yes	89.7% (84.4-93.5)	91.6% (88.6-93.9)	90.9% (88.5-93.0)	74.7% (63.1-83.7)	91.4% (88.9-93.4)	89.7% (87.2-91.8)
Length of service (Recoded)
5 years or less	87.8% (83.7-91.1)	88.6% (86.4-90.5)	88.4% (86.5-90.1)	80.8% (72.6-87.1)	90.7% (88.2-92.7)	89.2% (86.8-91.2)
6-10 years	81.7% (75.9-86.4)	89.0% (86.3-91.3)	87.1% (84.7-89.2)	77.9% (67.4-85.9)	88.4% (86.1-90.4)	87.5% (85.3-89.5)
More than 10 years	88.7% (86.8-90.4)	91.5% (90.6-92.4)	90.8% (90.0-91.6)	87.4% (83.5-90.5)	89.1% (88.4-89.8)	89.1% (88.3-89.7)
Healthcare setting (Recoded)
Outpatient care	85.2% (82.5-87.6)	89.3% (88.2-90.3)	88.6% (87.6-89.5)	89.9% (81.8-94.8)	88.3% (87.2-89.3)	88.3% (87.3-89.3)
In-hospital care	90.1% (87.7-92.0)	94.1% (92.7-95.2)	92.7% (91.5-93.7)	82.1% (71.4-89.5)	90.4% (89.4-91.4)	90.3% (89.2-91.2)
Emergencies	87.0% (81.8-90.9)	90.1% (86.4-92.8)	88.7% (85.9-91.1)	83.9% (79.9-87.3)	88.8% (87.0-90.4)	87.6% (86.0-89.1)
Proximity to combat areas and occupied territories
No conflict zone	87.3% (85.6-88.9)	90.3% (89.4-91.2)	89.5% (88.7-90.3)	86.3% (82.7-89.2)	89.0% (88.2-89.7)	88.8% (88.1-89.5)
Conflict zone	89.5% (84.9-92.8)	92.5% (90.7-94.0)	91.9% (90.2-93.3)	78.1% (69.2-85.1)	90.2% (88.7-91.6)	89.4% (87.9-90.8)
Unweighted Prevalence (95% CI)

table_all_exposures_u <- gen_exp_tbl_unweighted(ds_ua, exposures)

gtsave(table_all_exposures_u, "out/tables/all_exp_tbl_unweighted.docx")

table_all_exposures_u

Exposure	Doctor			Nurse
	Male (N = 2,496)	Female (N = 7,662)	Overall (N = 10,188)	Male (N = 835)	Female (N = 12,744)	Overall (N = 13,617)
Risk factor
Bullying	24.5% (22.7-26.3)	27.4% (26.3-28.5)	26.7% (25.8-27.6)	22.0% (19.0-25.2)	18.1% (17.4-18.8)	18.3% (17.6-19.0)
Sexual Harassment	8.2% (7.1-9.5)	9.8% (9.1-10.5)	9.4% (8.8-10.1)	8.8% (6.9-11.2)	6.8% (6.3-7.3)	6.9% (6.4-7.4)
Violent threats	27.2% (25.4-29.1)	17.6% (16.7-18.6)	20.1% (19.2-20.9)	29.0% (25.7-32.5)	10.2% (9.7-10.8)	11.4% (10.8-12.0)
Physical violence	5.5% (4.6-6.6)	2.5% (2.2-2.9)	3.3% (2.9-3.7)	10.9% (8.8-13.5)	3.1% (2.8-3.5)	3.6% (3.3-4.0)
Protective factor
Colleague support	58.7% (56.7-60.8)	59.8% (58.7-61.0)	59.5% (58.5-60.5)	54.6% (50.9-58.2)	58.3% (57.3-59.2)	58.1% (57.2-58.9)
Support from superiors	49.5% (47.5-51.6)	52.7% (51.5-53.9)	51.8% (50.8-52.9)	48.7% (45.0-52.4)	51.2% (50.3-52.2)	51.1% (50.2-52.0)
Meaning in work	81.3% (79.3-83.1)	84.5% (83.4-85.4)	83.6% (82.7-84.5)	68.6% (64.4-72.5)	80.6% (79.8-81.5)	79.9% (79.1-80.7)
Purpose in work	87.7% (86.0-89.1)	90.7% (89.9-91.5)	89.9% (89.2-90.6)	84.7% (81.4-87.4)	89.2% (88.5-89.8)	88.9% (88.3-89.5)
Unweighted Prevalence (95% CI)

table_all_outcomes_u <- gen_out_tbl_unweighted(ds_ua, outcomes)

gtsave(table_all_outcomes_u, "out/tables/all_out_tbl_unweighted.docx")

table_all_outcomes_u

Outcome	Doctor			Nurse
	Male (N = 2,496)	Female (N = 7,662)	Overall (N = 10,188)	Male (N = 835)	Female (N = 12,744)	Overall (N = 13,617)
Probable Depression (PHQ-9)	33.8% (31.8-35.9)	35.5% (34.4-36.7)	35.1% (34.1-36.2)	27.5% (24.2-31.0)	22.4% (21.6-23.3)	22.7% (21.9-23.5)
Probable Anxiety (GAD-7)	21.7% (20.0-23.5)	25.7% (24.6-26.8)	24.7% (23.8-25.7)	16.3% (13.7-19.4)	16.6% (15.9-17.3)	16.6% (15.9-17.3)
Passive suicide thoughts (Item 9 of PHQ-9)	15.2% (13.7-16.8)	11.4% (10.6-12.2)	12.3% (11.7-13.1)	12.6% (10.3-15.4)	8.0% (7.5-8.5)	8.3% (7.8-8.8)
Unweighted Prevalence (95% CI). PHQ-9: 9-item Patient Health Questionnaire, a cut-off score ≥ 10 is defined for detecting probable depression. GAD-7: 7 item anxiety scale, a cut-off score ≥ 10 is defined for detecting probable anxiety. Any positive answer in the 9th item of the PHQ-9 is considered as having passive suicide thoughts.

reg_tbl <- 
  
  ds_ua |> 
  select(loc_en, work_2, scdm_2_rec) |>
  tbl_strata(
    strata = "work_2",
    ~ tbl_summary(., 
                  by = "scdm_2_rec",
                  label = list(loc_en = "Region")) |> 
      add_overall()
    ) |> 
  as_gt()

## 30 missing rows in the "scdm_2_rec" column have been removed.
## 38 missing rows in the "scdm_2_rec" column have been removed.

reg_tbl

Characteristic	Doctor			Nurse
	Overall N = 10,1581	Female N = 7,6621	Male N = 2,4961	Overall N = 13,5791	Female N = 12,7441	Male N = 8351
Region
Cherkasy Oblast	394 (3.9%)	279 (3.6%)	115 (4.6%)	650 (4.8%)	632 (5.0%)	18 (2.2%)
Chernihiv Oblast	311 (3.1%)	250 (3.3%)	61 (2.4%)	675 (5.0%)	631 (5.0%)	44 (5.3%)
Chernivtsi Oblast	65 (0.6%)	47 (0.6%)	18 (0.7%)	65 (0.5%)	64 (0.5%)	1 (0.1%)
Crimea	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)
Dnipropetrovsk Oblast	1,123 (11%)	799 (10%)	324 (13%)	1,874 (14%)	1,651 (13%)	223 (27%)
Donetsk Oblast	42 (0.4%)	29 (0.4%)	13 (0.5%)	76 (0.6%)	72 (0.6%)	4 (0.5%)
Ivano-Frankivsk Oblast	504 (5.0%)	355 (4.6%)	149 (6.0%)	591 (4.4%)	570 (4.5%)	21 (2.5%)
Kharkiv Oblast	766 (7.5%)	602 (7.9%)	164 (6.6%)	1,159 (8.5%)	1,073 (8.4%)	86 (10%)
Kherson Oblast	82 (0.8%)	61 (0.8%)	21 (0.8%)	131 (1.0%)	129 (1.0%)	2 (0.2%)
Khmelnytskyi Oblast	346 (3.4%)	216 (2.8%)	130 (5.2%)	401 (3.0%)	392 (3.1%)	9 (1.1%)
Kirovohrad Oblast	548 (5.4%)	419 (5.5%)	129 (5.2%)	628 (4.6%)	595 (4.7%)	33 (4.0%)
Kyiv City	456 (4.5%)	347 (4.5%)	109 (4.4%)	304 (2.2%)	271 (2.1%)	33 (4.0%)
Kyiv Oblast	271 (2.7%)	215 (2.8%)	56 (2.2%)	205 (1.5%)	196 (1.5%)	9 (1.1%)
Luhansk Oblast	16 (0.2%)	13 (0.2%)	3 (0.1%)	6 (<0.1%)	6 (<0.1%)	0 (0%)
Lviv Oblast	1,331 (13%)	1,028 (13%)	303 (12%)	1,476 (11%)	1,438 (11%)	38 (4.6%)
Mykolaiv Oblast	226 (2.2%)	173 (2.3%)	53 (2.1%)	436 (3.2%)	400 (3.1%)	36 (4.3%)
Odesa Oblast	565 (5.6%)	435 (5.7%)	130 (5.2%)	853 (6.3%)	724 (5.7%)	129 (15%)
Poltava Oblast	483 (4.8%)	404 (5.3%)	79 (3.2%)	700 (5.2%)	682 (5.4%)	18 (2.2%)
Rivne Oblast	193 (1.9%)	148 (1.9%)	45 (1.8%)	208 (1.5%)	204 (1.6%)	4 (0.5%)
Sumy Oblast	202 (2.0%)	163 (2.1%)	39 (1.6%)	275 (2.0%)	267 (2.1%)	8 (1.0%)
Ternopil Oblast	551 (5.4%)	399 (5.2%)	152 (6.1%)	968 (7.1%)	931 (7.3%)	37 (4.4%)
Vinnytsia Oblast	143 (1.4%)	118 (1.5%)	25 (1.0%)	215 (1.6%)	211 (1.7%)	4 (0.5%)
Volyn Oblast	468 (4.6%)	353 (4.6%)	115 (4.6%)	607 (4.5%)	582 (4.6%)	25 (3.0%)
Zakarpattia Oblast	235 (2.3%)	152 (2.0%)	83 (3.3%)	178 (1.3%)	154 (1.2%)	24 (2.9%)
Zaporizhzhia Oblast	299 (2.9%)	240 (3.1%)	59 (2.4%)	226 (1.7%)	221 (1.7%)	5 (0.6%)
Zhytomyr Oblast	538 (5.3%)	417 (5.4%)	121 (4.8%)	672 (4.9%)	648 (5.1%)	24 (2.9%)
1 n (%)

gtsave(reg_tbl, "out/tables/tbl_ukr_reg.docx")

vio_exp_agg <- c("work_30_dic", "work_31_dic", "work_32_dic",
                 "work_33_dic")

prot_fac_agg <- c("work_21_dic", "work_22_dic", "wb_wami_1_dic",
                  "wb_wami_2_dic")

out_prev_agg <- c("phq_co", "gad_co", "suic_idea")

exp_reg <-
  
  ds_ua |> 
  group_by(loc_en) |> 
  summarise(
    across(
      all_of(vio_exp_agg),
      ~ mean(. == "Yes", na.rm = TRUE)
    )
  ) |> 
  add_row(tibble_row(loc_en = "Crimea")) |> 
  left_join(ua_reg_conflict |> select(loc_3, loc_en, fips_code),
            by = "loc_en")

exp_reg

## # A tibble: 26 × 7
##    loc_en        work_30_dic work_31_dic work_32_dic work_33_dic loc_3 fips_code
##    <chr>               <dbl>       <dbl>       <dbl>       <dbl> <fct> <chr>    
##  1 Cherkasy Obl…      0.113       0.171       0.0432       0.253 Черк… UP01     
##  2 Chernihiv Ob…      0.0641      0.160       0.0355       0.232 Черн… UP02     
##  3 Chernivtsi O…      0.0667      0.143       0.0288       0.269 Черн… UP03     
##  4 Dnipropetrov…      0.0809      0.199       0.0594       0.194 Дніп… UP04     
##  5 Donetsk Obla…      0.0550      0.0926      0.0278       0.148 Доне… UP05     
##  6 Ivano-Franki…      0.0825      0.126       0.0251       0.248 Іван… UP06     
##  7 Kharkiv Obla…      0.0828      0.127       0.0352       0.199 Харк… UP07     
##  8 Kherson Obla…      0.0659      0.120       0.0180       0.156 Херс… UP08     
##  9 Khmelnytskyi…      0.116       0.188       0.0569       0.268 Хмел… UP09     
## 10 Kirovohrad O…      0.0847      0.169       0.0297       0.245 Кіро… UP10     
## # ℹ 16 more rows

saveRDS(exp_reg, "out/dataframes/exposure_regions.rds")
  
out_reg <-
  
  ds_ua |> 
  group_by(loc_en) |> 
  summarise(
    across(
      all_of(out_prev_agg),
      ~ mean(. == 1, na.rm = TRUE)
    )
  ) |> 
  add_row(tibble_row(loc_en = "Crimea"))|> 
  left_join(ua_reg_conflict |> select(loc_3, loc_en, fips_code),
            by = "loc_en")

out_reg

## # A tibble: 26 × 6
##    loc_en                 phq_co gad_co suic_idea loc_3                fips_code
##    <chr>                   <dbl>  <dbl>     <dbl> <fct>                <chr>    
##  1 Cherkasy Oblast         0.309  0.216    0.117  Черкаська Область    UP01     
##  2 Chernihiv Oblast        0.325  0.244    0.120  Чернігівська Область UP02     
##  3 Chernivtsi Oblast       0.218  0.149    0.0990 Чернівецька Область  UP03     
##  4 Dnipropetrovsk Oblast   0.274  0.198    0.101  Дніпропетровська Об… UP04     
##  5 Donetsk Oblast          0.264  0.189    0.104  Донецька Область     UP05     
##  6 Ivano-Frankivsk Oblast  0.266  0.192    0.0991 Івано-Франківська О… UP06     
##  7 Kharkiv Oblast          0.270  0.204    0.0982 Харківська Область   UP07     
##  8 Kherson Oblast          0.276  0.220    0.0848 Херсонська Область   UP08     
##  9 Khmelnytskyi Oblast     0.327  0.221    0.114  Хмельницька Область  UP09     
## 10 Kirovohrad Oblast       0.266  0.204    0.106  Кіровоградська Обла… UP10     
## # ℹ 16 more rows

saveRDS(out_reg, "out/dataframes/outcome_regions.rds")

Generalized Linear Models with Multilevel variables

Run models

# define covariates
covariate_map <- list(
  "peer" = c("conflict", "scdm_1_rec", "scdm_2_rec", "work_2", "work_5_rec",
             "work_3_rec", "work_1"),
  
  "viol" = c("conflict", "scdm_1_rec", "scdm_2_rec", "work_2", "work_5_rec"),
  
  "har_bul" = c("conflict", "scdm_1_rec", "scdm_2_rec", "work_2", "work_1",
                "work_3_rec", "work_5_rec"),
  
  "meanpur" = c("conflict", "scdm_1_rec", "scdm_2_rec", "work_2",
                "work_5_rec", "work_3_rec", "work_1", "scdm_4_dic",
                "scdm_5_dic"),
  
  "structure" = c("conflict", "scdm_1_rec", "scdm_2_rec", "work_2")
)

analysis_plan <- tribble(
  ~Exposure,     ~Covar_Key,
  "work_21_dic", "peer",
  "work_22_dic", "peer",
  "work_31_dic", "viol",
  "work_32_dic", "viol",
  "work_30_dic", "har_bul",
  "work_33_dic", "har_bul",
  "wb_wami_1_dic", "meanpur",
  "wb_wami_2_dic", "meanpur"
)

outcomes_map <- tribble(
  ~Outcome,    ~Family,
  "phq_co",    "poisson",
  "gad_co",    "poisson",
  "suic_idea",    "poisson",
)

# Define stratification of models
profs <- c("Doctor", "Nurse")

# Generate exposure-outcome binomials
tasks <- expand_grid(
  out_row = outcomes_map,
  exp_row = analysis_plan,
  profession = profs
) |> 
  tidyr::unpack(cols = c(out_row, exp_row))


all_runs_list <- pmap(list(
  tasks$Outcome,
  tasks$Family,
  tasks$Exposure,
  tasks$Covar_Key,
  tasks$profession
), function(outcome, family, exposure, cov_key, prof) {
  
  message(paste("Processing:", outcome, "x", exposure, "in", prof))
  
  res_crude <- run_crude_glm(
    data = ds_ua, 
    outcome_var = outcome,
    exposure_var = exposure, 
    profession = prof,
    cluster_var = "loc_3", 
    family_type = family,
    )

  

  res_str <- run_glm_adj(
    data = ds_ua, 
    outcome_var = outcome, 
    exposure_var = exposure,
    confounder_list = covariate_map[["structure"]], 
    profession = prof,
    cluster_var = "loc_3", 
    family_type = family, 
    model_label = "Partially adjusted"
    )
  
  #look up for specific covariate list
  specific_covars <- covariate_map[[cov_key]]
  
  res_adj <- run_glm_adj(
    data = ds_ua, 
    outcome_var = outcome, 
    exposure_var = exposure,
    confounder_list = specific_covars, 
    profession = prof,
    cluster_var = "loc_3", 
    family_type = family, 
    model_label = "Fully adjusted"
    )
  
  
  # Combine internally
  row_multilevel <- 
    
    bind_rows(
      res_crude$multilevel,
      res_str$multilevel,
      res_adj$multilevel
    )
  
  row_robust <- 
    
    bind_rows(
      res_crude$robust,
      res_str$robust,
      res_adj$robust
    )
  
  # return as clean pair
  return(list(
    multilevel = row_multilevel,
    robust     = row_robust
  ))
})

## Processing: phq_co x work_21_dic in Doctor

## Loading ML model: glm_dep_cols_doc_cr_ml.rds

## Loading Robust model: glm_dep_cols_doc_cr_rob.rds

## Loading ML model: glm_dep_cols_doc_str_ml.rds

## Loading Robust SE model: glm_dep_cols_doc_str_rob.rds

## Loading ML model: glm_dep_cols_doc_adj_ml.rds

## Loading Robust SE model: glm_dep_cols_doc_adj_rob.rds

## Processing: phq_co x work_21_dic in Nurse

## Loading ML model: glm_dep_cols_nur_cr_ml.rds

## Loading Robust model: glm_dep_cols_nur_cr_rob.rds

## Loading ML model: glm_dep_cols_nur_str_ml.rds

## Loading Robust SE model: glm_dep_cols_nur_str_rob.rds

## Loading ML model: glm_dep_cols_nur_adj_ml.rds

## Loading Robust SE model: glm_dep_cols_nur_adj_rob.rds

## Processing: phq_co x work_22_dic in Doctor

## Loading ML model: glm_dep_sup_doc_cr_ml.rds

## Loading Robust model: glm_dep_sup_doc_cr_rob.rds

## Loading ML model: glm_dep_sup_doc_str_ml.rds

## Loading Robust SE model: glm_dep_sup_doc_str_rob.rds

## Loading ML model: glm_dep_sup_doc_adj_ml.rds

## Loading Robust SE model: glm_dep_sup_doc_adj_rob.rds

## Processing: phq_co x work_22_dic in Nurse

## Loading ML model: glm_dep_sup_nur_cr_ml.rds

## Loading Robust model: glm_dep_sup_nur_cr_rob.rds

## Loading ML model: glm_dep_sup_nur_str_ml.rds

## Loading Robust SE model: glm_dep_sup_nur_str_rob.rds

## Loading ML model: glm_dep_sup_nur_adj_ml.rds

## Loading Robust SE model: glm_dep_sup_nur_adj_rob.rds

## Processing: phq_co x work_31_dic in Doctor

## Loading ML model: glm_dep_threats_doc_cr_ml.rds

## Loading Robust model: glm_dep_threats_doc_cr_rob.rds

## Loading ML model: glm_dep_threats_doc_str_ml.rds

## Loading Robust SE model: glm_dep_threats_doc_str_rob.rds

## Loading ML model: glm_dep_threats_doc_adj_ml.rds

## Loading Robust SE model: glm_dep_threats_doc_adj_rob.rds

## Processing: phq_co x work_31_dic in Nurse

## Loading ML model: glm_dep_threats_nur_cr_ml.rds

## Loading Robust model: glm_dep_threats_nur_cr_rob.rds

## Loading ML model: glm_dep_threats_nur_str_ml.rds

## Loading Robust SE model: glm_dep_threats_nur_str_rob.rds

## Loading ML model: glm_dep_threats_nur_adj_ml.rds

## Loading Robust SE model: glm_dep_threats_nur_adj_rob.rds

## Processing: phq_co x work_32_dic in Doctor

## Loading ML model: glm_dep_viol_doc_cr_ml.rds

## Loading Robust model: glm_dep_viol_doc_cr_rob.rds

## Loading ML model: glm_dep_viol_doc_str_ml.rds

## Loading Robust SE model: glm_dep_viol_doc_str_rob.rds

## Loading ML model: glm_dep_viol_doc_adj_ml.rds

## Loading Robust SE model: glm_dep_viol_doc_adj_rob.rds

## Processing: phq_co x work_32_dic in Nurse

## Loading ML model: glm_dep_viol_nur_cr_ml.rds

## Loading Robust model: glm_dep_viol_nur_cr_rob.rds

## Loading ML model: glm_dep_viol_nur_str_ml.rds

## Loading Robust SE model: glm_dep_viol_nur_str_rob.rds

## Loading ML model: glm_dep_viol_nur_adj_ml.rds

## Loading Robust SE model: glm_dep_viol_nur_adj_rob.rds

## Processing: phq_co x work_30_dic in Doctor

## Loading ML model: glm_dep_har_doc_cr_ml.rds

## Loading Robust model: glm_dep_har_doc_cr_rob.rds

## Loading ML model: glm_dep_har_doc_str_ml.rds

## Loading Robust SE model: glm_dep_har_doc_str_rob.rds

## Loading ML model: glm_dep_har_doc_adj_ml.rds

## Loading Robust SE model: glm_dep_har_doc_adj_rob.rds

## Processing: phq_co x work_30_dic in Nurse

## Loading ML model: glm_dep_har_nur_cr_ml.rds

## Loading Robust model: glm_dep_har_nur_cr_rob.rds

## Loading ML model: glm_dep_har_nur_str_ml.rds

## Loading Robust SE model: glm_dep_har_nur_str_rob.rds

## Loading ML model: glm_dep_har_nur_adj_ml.rds

## Loading Robust SE model: glm_dep_har_nur_adj_rob.rds

## Processing: phq_co x work_33_dic in Doctor

## Loading ML model: glm_dep_bul_doc_cr_ml.rds

## Loading Robust model: glm_dep_bul_doc_cr_rob.rds

## Loading ML model: glm_dep_bul_doc_str_ml.rds

## Loading Robust SE model: glm_dep_bul_doc_str_rob.rds

## Loading ML model: glm_dep_bul_doc_adj_ml.rds

## Loading Robust SE model: glm_dep_bul_doc_adj_rob.rds

## Processing: phq_co x work_33_dic in Nurse

## Loading ML model: glm_dep_bul_nur_cr_ml.rds

## Loading Robust model: glm_dep_bul_nur_cr_rob.rds

## Loading ML model: glm_dep_bul_nur_str_ml.rds

## Loading Robust SE model: glm_dep_bul_nur_str_rob.rds

## Loading ML model: glm_dep_bul_nur_adj_ml.rds

## Loading Robust SE model: glm_dep_bul_nur_adj_rob.rds

## Processing: phq_co x wb_wami_1_dic in Doctor

## Loading ML model: glm_dep_mean_doc_cr_ml.rds

## Loading Robust model: glm_dep_mean_doc_cr_rob.rds

## Loading ML model: glm_dep_mean_doc_str_ml.rds

## Loading Robust SE model: glm_dep_mean_doc_str_rob.rds

## Loading ML model: glm_dep_mean_doc_adj_ml.rds

## Loading Robust SE model: glm_dep_mean_doc_adj_rob.rds

## Processing: phq_co x wb_wami_1_dic in Nurse

## Loading ML model: glm_dep_mean_nur_cr_ml.rds

## Loading Robust model: glm_dep_mean_nur_cr_rob.rds

## Loading ML model: glm_dep_mean_nur_str_ml.rds

## Loading Robust SE model: glm_dep_mean_nur_str_rob.rds

## Loading ML model: glm_dep_mean_nur_adj_ml.rds

## Loading Robust SE model: glm_dep_mean_nur_adj_rob.rds

## Processing: phq_co x wb_wami_2_dic in Doctor

## Loading ML model: glm_dep_purp_doc_cr_ml.rds

## Loading Robust model: glm_dep_purp_doc_cr_rob.rds

## Loading ML model: glm_dep_purp_doc_str_ml.rds

## Loading Robust SE model: glm_dep_purp_doc_str_rob.rds

## Loading ML model: glm_dep_purp_doc_adj_ml.rds

## Loading Robust SE model: glm_dep_purp_doc_adj_rob.rds

## Processing: phq_co x wb_wami_2_dic in Nurse

## Loading ML model: glm_dep_purp_nur_cr_ml.rds

## Loading Robust model: glm_dep_purp_nur_cr_rob.rds

## Loading ML model: glm_dep_purp_nur_str_ml.rds

## Loading Robust SE model: glm_dep_purp_nur_str_rob.rds

## Loading ML model: glm_dep_purp_nur_adj_ml.rds

## Loading Robust SE model: glm_dep_purp_nur_adj_rob.rds

## Processing: gad_co x work_21_dic in Doctor

## Loading ML model: glm_anx_cols_doc_cr_ml.rds

## Loading Robust model: glm_anx_cols_doc_cr_rob.rds

## Loading ML model: glm_anx_cols_doc_str_ml.rds

## Loading Robust SE model: glm_anx_cols_doc_str_rob.rds

## Loading ML model: glm_anx_cols_doc_adj_ml.rds

## Loading Robust SE model: glm_anx_cols_doc_adj_rob.rds

## Processing: gad_co x work_21_dic in Nurse

## Loading ML model: glm_anx_cols_nur_cr_ml.rds

## Loading Robust model: glm_anx_cols_nur_cr_rob.rds

## Loading ML model: glm_anx_cols_nur_str_ml.rds

## Loading Robust SE model: glm_anx_cols_nur_str_rob.rds

## Loading ML model: glm_anx_cols_nur_adj_ml.rds

## Loading Robust SE model: glm_anx_cols_nur_adj_rob.rds

## Processing: gad_co x work_22_dic in Doctor

## Loading ML model: glm_anx_sup_doc_cr_ml.rds

## Loading Robust model: glm_anx_sup_doc_cr_rob.rds

## Loading ML model: glm_anx_sup_doc_str_ml.rds

## Loading Robust SE model: glm_anx_sup_doc_str_rob.rds

## Loading ML model: glm_anx_sup_doc_adj_ml.rds

## Loading Robust SE model: glm_anx_sup_doc_adj_rob.rds

## Processing: gad_co x work_22_dic in Nurse

## Loading ML model: glm_anx_sup_nur_cr_ml.rds

## Loading Robust model: glm_anx_sup_nur_cr_rob.rds

## Loading ML model: glm_anx_sup_nur_str_ml.rds

## Loading Robust SE model: glm_anx_sup_nur_str_rob.rds

## Loading ML model: glm_anx_sup_nur_adj_ml.rds

## Loading Robust SE model: glm_anx_sup_nur_adj_rob.rds

## Processing: gad_co x work_31_dic in Doctor

## Loading ML model: glm_anx_threats_doc_cr_ml.rds

## Loading Robust model: glm_anx_threats_doc_cr_rob.rds

## Loading ML model: glm_anx_threats_doc_str_ml.rds

## Loading Robust SE model: glm_anx_threats_doc_str_rob.rds

## Loading ML model: glm_anx_threats_doc_adj_ml.rds

## Loading Robust SE model: glm_anx_threats_doc_adj_rob.rds

## Processing: gad_co x work_31_dic in Nurse

## Loading ML model: glm_anx_threats_nur_cr_ml.rds

## Loading Robust model: glm_anx_threats_nur_cr_rob.rds

## Loading ML model: glm_anx_threats_nur_str_ml.rds

## Loading Robust SE model: glm_anx_threats_nur_str_rob.rds

## Loading ML model: glm_anx_threats_nur_adj_ml.rds

## Loading Robust SE model: glm_anx_threats_nur_adj_rob.rds

## Processing: gad_co x work_32_dic in Doctor

## Loading ML model: glm_anx_viol_doc_cr_ml.rds

## Loading Robust model: glm_anx_viol_doc_cr_rob.rds

## Loading ML model: glm_anx_viol_doc_str_ml.rds

## Loading Robust SE model: glm_anx_viol_doc_str_rob.rds

## Loading ML model: glm_anx_viol_doc_adj_ml.rds

## Loading Robust SE model: glm_anx_viol_doc_adj_rob.rds

## Processing: gad_co x work_32_dic in Nurse

## Loading ML model: glm_anx_viol_nur_cr_ml.rds

## Loading Robust model: glm_anx_viol_nur_cr_rob.rds

## Loading ML model: glm_anx_viol_nur_str_ml.rds

## Loading Robust SE model: glm_anx_viol_nur_str_rob.rds

## Loading ML model: glm_anx_viol_nur_adj_ml.rds

## Loading Robust SE model: glm_anx_viol_nur_adj_rob.rds

## Processing: gad_co x work_30_dic in Doctor

## Loading ML model: glm_anx_har_doc_cr_ml.rds

## Loading Robust model: glm_anx_har_doc_cr_rob.rds

## Loading ML model: glm_anx_har_doc_str_ml.rds

## Loading Robust SE model: glm_anx_har_doc_str_rob.rds

## Loading ML model: glm_anx_har_doc_adj_ml.rds

## Loading Robust SE model: glm_anx_har_doc_adj_rob.rds

## Processing: gad_co x work_30_dic in Nurse

## Loading ML model: glm_anx_har_nur_cr_ml.rds

## Loading Robust model: glm_anx_har_nur_cr_rob.rds

## Loading ML model: glm_anx_har_nur_str_ml.rds

## Loading Robust SE model: glm_anx_har_nur_str_rob.rds

## Loading ML model: glm_anx_har_nur_adj_ml.rds

## Loading Robust SE model: glm_anx_har_nur_adj_rob.rds

## Processing: gad_co x work_33_dic in Doctor

## Loading ML model: glm_anx_bul_doc_cr_ml.rds

## Loading Robust model: glm_anx_bul_doc_cr_rob.rds

## Loading ML model: glm_anx_bul_doc_str_ml.rds

## Loading Robust SE model: glm_anx_bul_doc_str_rob.rds

## Loading ML model: glm_anx_bul_doc_adj_ml.rds

## Loading Robust SE model: glm_anx_bul_doc_adj_rob.rds

## Processing: gad_co x work_33_dic in Nurse

## Loading ML model: glm_anx_bul_nur_cr_ml.rds

## Loading Robust model: glm_anx_bul_nur_cr_rob.rds

## Loading ML model: glm_anx_bul_nur_str_ml.rds

## Loading Robust SE model: glm_anx_bul_nur_str_rob.rds

## Loading ML model: glm_anx_bul_nur_adj_ml.rds

## Loading Robust SE model: glm_anx_bul_nur_adj_rob.rds

## Processing: gad_co x wb_wami_1_dic in Doctor

## Loading ML model: glm_anx_mean_doc_cr_ml.rds

## Loading Robust model: glm_anx_mean_doc_cr_rob.rds

## Loading ML model: glm_anx_mean_doc_str_ml.rds

## Loading Robust SE model: glm_anx_mean_doc_str_rob.rds

## Loading ML model: glm_anx_mean_doc_adj_ml.rds

## Loading Robust SE model: glm_anx_mean_doc_adj_rob.rds

## Processing: gad_co x wb_wami_1_dic in Nurse

## Loading ML model: glm_anx_mean_nur_cr_ml.rds

## Loading Robust model: glm_anx_mean_nur_cr_rob.rds

## Loading ML model: glm_anx_mean_nur_str_ml.rds

## Loading Robust SE model: glm_anx_mean_nur_str_rob.rds

## Loading ML model: glm_anx_mean_nur_adj_ml.rds

## Loading Robust SE model: glm_anx_mean_nur_adj_rob.rds

## Processing: gad_co x wb_wami_2_dic in Doctor

## Loading ML model: glm_anx_purp_doc_cr_ml.rds

## Loading Robust model: glm_anx_purp_doc_cr_rob.rds

## Loading ML model: glm_anx_purp_doc_str_ml.rds

## Loading Robust SE model: glm_anx_purp_doc_str_rob.rds

## Loading ML model: glm_anx_purp_doc_adj_ml.rds

## Loading Robust SE model: glm_anx_purp_doc_adj_rob.rds

## Processing: gad_co x wb_wami_2_dic in Nurse

## Loading ML model: glm_anx_purp_nur_cr_ml.rds

## Loading Robust model: glm_anx_purp_nur_cr_rob.rds

## Loading ML model: glm_anx_purp_nur_str_ml.rds

## Loading Robust SE model: glm_anx_purp_nur_str_rob.rds

## Loading ML model: glm_anx_purp_nur_adj_ml.rds

## Loading Robust SE model: glm_anx_purp_nur_adj_rob.rds

## Processing: suic_idea x work_21_dic in Doctor

## Loading ML model: glm_suic_cols_doc_cr_ml.rds

## Loading Robust model: glm_suic_cols_doc_cr_rob.rds

## Loading ML model: glm_suic_cols_doc_str_ml.rds

## Loading Robust SE model: glm_suic_cols_doc_str_rob.rds

## Loading ML model: glm_suic_cols_doc_adj_ml.rds

## Loading Robust SE model: glm_suic_cols_doc_adj_rob.rds

## Processing: suic_idea x work_21_dic in Nurse

## Loading ML model: glm_suic_cols_nur_cr_ml.rds

## Loading Robust model: glm_suic_cols_nur_cr_rob.rds

## Loading ML model: glm_suic_cols_nur_str_ml.rds

## Loading Robust SE model: glm_suic_cols_nur_str_rob.rds

## Loading ML model: glm_suic_cols_nur_adj_ml.rds

## Loading Robust SE model: glm_suic_cols_nur_adj_rob.rds

## Processing: suic_idea x work_22_dic in Doctor

## Loading ML model: glm_suic_sup_doc_cr_ml.rds

## Loading Robust model: glm_suic_sup_doc_cr_rob.rds

## Loading ML model: glm_suic_sup_doc_str_ml.rds

## Loading Robust SE model: glm_suic_sup_doc_str_rob.rds

## Loading ML model: glm_suic_sup_doc_adj_ml.rds

## Loading Robust SE model: glm_suic_sup_doc_adj_rob.rds

## Processing: suic_idea x work_22_dic in Nurse

## Loading ML model: glm_suic_sup_nur_cr_ml.rds

## Loading Robust model: glm_suic_sup_nur_cr_rob.rds

## Loading ML model: glm_suic_sup_nur_str_ml.rds

## Loading Robust SE model: glm_suic_sup_nur_str_rob.rds

## Loading ML model: glm_suic_sup_nur_adj_ml.rds

## Loading Robust SE model: glm_suic_sup_nur_adj_rob.rds

## Processing: suic_idea x work_31_dic in Doctor

## Loading ML model: glm_suic_threats_doc_cr_ml.rds

## Loading Robust model: glm_suic_threats_doc_cr_rob.rds

## Loading ML model: glm_suic_threats_doc_str_ml.rds

## Loading Robust SE model: glm_suic_threats_doc_str_rob.rds

## Loading ML model: glm_suic_threats_doc_adj_ml.rds

## Loading Robust SE model: glm_suic_threats_doc_adj_rob.rds

## Processing: suic_idea x work_31_dic in Nurse

## Loading ML model: glm_suic_threats_nur_cr_ml.rds

## Loading Robust model: glm_suic_threats_nur_cr_rob.rds

## Loading ML model: glm_suic_threats_nur_str_ml.rds

## Loading Robust SE model: glm_suic_threats_nur_str_rob.rds

## Loading ML model: glm_suic_threats_nur_adj_ml.rds

## Loading Robust SE model: glm_suic_threats_nur_adj_rob.rds

## Processing: suic_idea x work_32_dic in Doctor

## Loading ML model: glm_suic_viol_doc_cr_ml.rds

## Loading Robust model: glm_suic_viol_doc_cr_rob.rds

## Loading ML model: glm_suic_viol_doc_str_ml.rds

## Loading Robust SE model: glm_suic_viol_doc_str_rob.rds

## Loading ML model: glm_suic_viol_doc_adj_ml.rds

## Loading Robust SE model: glm_suic_viol_doc_adj_rob.rds

## Processing: suic_idea x work_32_dic in Nurse

## Loading ML model: glm_suic_viol_nur_cr_ml.rds

## Loading Robust model: glm_suic_viol_nur_cr_rob.rds

## Loading ML model: glm_suic_viol_nur_str_ml.rds

## Loading Robust SE model: glm_suic_viol_nur_str_rob.rds

## Loading ML model: glm_suic_viol_nur_adj_ml.rds

## Loading Robust SE model: glm_suic_viol_nur_adj_rob.rds

## Processing: suic_idea x work_30_dic in Doctor

## Loading ML model: glm_suic_har_doc_cr_ml.rds

## Loading Robust model: glm_suic_har_doc_cr_rob.rds

## Loading ML model: glm_suic_har_doc_str_ml.rds

## Loading Robust SE model: glm_suic_har_doc_str_rob.rds

## Loading ML model: glm_suic_har_doc_adj_ml.rds

## Loading Robust SE model: glm_suic_har_doc_adj_rob.rds

## Processing: suic_idea x work_30_dic in Nurse

## Loading ML model: glm_suic_har_nur_cr_ml.rds

## Loading Robust model: glm_suic_har_nur_cr_rob.rds

## Loading ML model: glm_suic_har_nur_str_ml.rds

## Loading Robust SE model: glm_suic_har_nur_str_rob.rds

## Loading ML model: glm_suic_har_nur_adj_ml.rds

## Loading Robust SE model: glm_suic_har_nur_adj_rob.rds

## Processing: suic_idea x work_33_dic in Doctor

## Loading ML model: glm_suic_bul_doc_cr_ml.rds

## Loading Robust model: glm_suic_bul_doc_cr_rob.rds

## Loading ML model: glm_suic_bul_doc_str_ml.rds

## Loading Robust SE model: glm_suic_bul_doc_str_rob.rds

## Loading ML model: glm_suic_bul_doc_adj_ml.rds

## Loading Robust SE model: glm_suic_bul_doc_adj_rob.rds

## Processing: suic_idea x work_33_dic in Nurse

## Loading ML model: glm_suic_bul_nur_cr_ml.rds

## Loading Robust model: glm_suic_bul_nur_cr_rob.rds

## Loading ML model: glm_suic_bul_nur_str_ml.rds

## Loading Robust SE model: glm_suic_bul_nur_str_rob.rds

## Loading ML model: glm_suic_bul_nur_adj_ml.rds

## Loading Robust SE model: glm_suic_bul_nur_adj_rob.rds

## Processing: suic_idea x wb_wami_1_dic in Doctor

## Loading ML model: glm_suic_mean_doc_cr_ml.rds

## Loading Robust model: glm_suic_mean_doc_cr_rob.rds

## Loading ML model: glm_suic_mean_doc_str_ml.rds

## Loading Robust SE model: glm_suic_mean_doc_str_rob.rds

## Loading ML model: glm_suic_mean_doc_adj_ml.rds

## Loading Robust SE model: glm_suic_mean_doc_adj_rob.rds

## Processing: suic_idea x wb_wami_1_dic in Nurse

## Loading ML model: glm_suic_mean_nur_cr_ml.rds

## Loading Robust model: glm_suic_mean_nur_cr_rob.rds

## Loading ML model: glm_suic_mean_nur_str_ml.rds

## Loading Robust SE model: glm_suic_mean_nur_str_rob.rds

## Loading ML model: glm_suic_mean_nur_adj_ml.rds

## Loading Robust SE model: glm_suic_mean_nur_adj_rob.rds

## Processing: suic_idea x wb_wami_2_dic in Doctor

## Loading ML model: glm_suic_purp_doc_cr_ml.rds

## Loading Robust model: glm_suic_purp_doc_cr_rob.rds

## Loading ML model: glm_suic_purp_doc_str_ml.rds

## Loading Robust SE model: glm_suic_purp_doc_str_rob.rds

## Loading ML model: glm_suic_purp_doc_adj_ml.rds

## Loading Robust SE model: glm_suic_purp_doc_adj_rob.rds

## Processing: suic_idea x wb_wami_2_dic in Nurse

## Loading ML model: glm_suic_purp_nur_cr_ml.rds

## Loading Robust model: glm_suic_purp_nur_cr_rob.rds

## Loading ML model: glm_suic_purp_nur_str_ml.rds

## Loading Robust SE model: glm_suic_purp_nur_str_rob.rds

## Loading ML model: glm_suic_purp_nur_adj_ml.rds

## Loading Robust SE model: glm_suic_purp_nur_adj_rob.rds

# Unzip and combine

glm_multilevel_df <- map_dfr(all_runs_list, "multilevel")

glm_robust_df <- map_dfr(all_runs_list, "robust")


# Save

saveRDS(glm_multilevel_df, "out/dataframes/glm_multilevel_df.rds")
writexl::write_xlsx(glm_multilevel_df,
                    "out/dataframes/glm_multilevel_df.xlsx")


saveRDS(glm_robust_df, "out/dataframes/glm_robust_df.rds")
writexl::write_xlsx(glm_robust_df, "out/dataframes/glm_robust_df.xlsx")

GLM results

# SE Ratios
glm_se_ratios <-
  
  glm_multilevel_df |>
  select(Outcome, Exposure, Profession, Adjustment, beta, PR, SE) |> 
  rename(`PR (ML)` = PR,
         `SE (ML)` = SE) |> 
  left_join(glm_robust_df |> 
              select(Outcome, Exposure, Profession, Adjustment, SE),
            by = c("Outcome", "Exposure", "Profession", "Adjustment")) |> 
  rename(`SE (Robust)` = SE) |> 
  mutate(`SE Ratio` = `SE (Robust)` / `SE (ML)`) |> 
  mutate(
    Exposure = case_when(
      Exposure == "work_21_dic" ~ "Colleague support",
      Exposure == "work_22_dic" ~ "Support from superiors",
      Exposure == "work_31_dic" ~ "Violent threats",
      Exposure == "work_32_dic" ~ "Physical violence",
      Exposure == "work_30_dic" ~ "Sexual Harassment",
      Exposure == "work_33_dic" ~ "Bullying",
      Exposure == "wb_wami_1_dic" ~ "Meaning in work",
      Exposure == "wb_wami_2_dic" ~ "Purpose in work",
      TRUE ~ Exposure),
    Outcome = case_when(
      Outcome == "phq_co" ~ "Depression",
      Outcome == "gad_co" ~ "Anxiety",
      Outcome == "suic_idea" ~ "Suicide thoughts",
      TRUE ~ Outcome
    )
  )

glm_se_ratios

## # A tibble: 144 × 9
##    Outcome    Exposure          Profession Adjustment   beta `PR (ML)` `SE (ML)`
##    <chr>      <chr>             <chr>      <chr>       <dbl>     <dbl>     <dbl>
##  1 Depression Colleague support Doctor     Crude      -0.458     0.633    0.0365
##  2 Depression Colleague support Doctor     Partially… -0.416     0.660    0.0366
##  3 Depression Colleague support Doctor     Fully adj… -0.419     0.658    0.0368
##  4 Depression Colleague support Nurse      Crude      -0.544     0.580    0.0402
##  5 Depression Colleague support Nurse      Partially… -0.519     0.595    0.0403
##  6 Depression Colleague support Nurse      Fully adj… -0.519     0.595    0.0404
##  7 Depression Support from sup… Doctor     Crude      -0.539     0.583    0.0375
##  8 Depression Support from sup… Doctor     Partially… -0.483     0.617    0.0376
##  9 Depression Support from sup… Doctor     Fully adj… -0.481     0.618    0.0378
## 10 Depression Support from sup… Nurse      Crude      -0.701     0.496    0.0422
## # ℹ 134 more rows
## # ℹ 2 more variables: `SE (Robust)` <dbl>, `SE Ratio` <dbl>

# Save

saveRDS(glm_se_ratios, "out/dataframes/glm_se_ratios_df.rds")
writexl::write_xlsx(glm_se_ratios, "out/dataframes/glm_se_ratios_df.xlsx")


# Results with ML estimator + SE robust

glm_results <-
  
  glm_multilevel_df |> 
  select(-c(model, N_Analysis, Risk_Unexp, Risk_Exp)) |> 
  mutate(
    Exposure = case_when(
      Exposure == "work_21_dic" ~ "Colleague support",
      Exposure == "work_22_dic" ~ "Support from superiors",
      Exposure == "work_31_dic" ~ "Violent threats",
      Exposure == "work_32_dic" ~ "Physical violence",
      Exposure == "work_30_dic" ~ "Sexual Harassment",
      Exposure == "work_33_dic" ~ "Bullying",
      Exposure == "wb_wami_1_dic" ~ "Meaning in work",
      Exposure == "wb_wami_2_dic" ~ "Purpose in work",
      TRUE ~ Exposure),
    Outcome = case_when(
      Outcome == "phq_co" ~ "Depression",
      Outcome == "gad_co" ~ "Anxiety",
      Outcome == "suic_idea" ~ "Suicide thoughts",
      TRUE ~ Outcome
    )
  ) |> 
  mutate(
     Est_CI = sprintf("%.2f (%.2f, %.2f)", PR, CI_l, CI_u)
  )

glm_results

## # A tibble: 144 × 10
##    Outcome Exposure Profession Adjustment   beta     SE    PR  CI_l  CI_u Est_CI
##    <chr>   <chr>    <chr>      <chr>       <dbl>  <dbl> <dbl> <dbl> <dbl> <chr> 
##  1 Depres… Colleag… Doctor     Crude      -0.458 0.0365 0.633 0.589 0.679 0.63 …
##  2 Depres… Colleag… Doctor     Partially… -0.416 0.0366 0.660 0.614 0.709 0.66 …
##  3 Depres… Colleag… Doctor     Fully adj… -0.419 0.0368 0.658 0.612 0.707 0.66 …
##  4 Depres… Colleag… Nurse      Crude      -0.544 0.0402 0.580 0.536 0.628 0.58 …
##  5 Depres… Colleag… Nurse      Partially… -0.519 0.0403 0.595 0.550 0.644 0.59 …
##  6 Depres… Colleag… Nurse      Fully adj… -0.519 0.0404 0.595 0.550 0.644 0.59 …
##  7 Depres… Support… Doctor     Crude      -0.539 0.0375 0.583 0.542 0.627 0.58 …
##  8 Depres… Support… Doctor     Partially… -0.483 0.0376 0.617 0.573 0.664 0.62 …
##  9 Depres… Support… Doctor     Fully adj… -0.481 0.0378 0.618 0.574 0.666 0.62 …
## 10 Depres… Support… Nurse      Crude      -0.701 0.0422 0.496 0.457 0.539 0.50 …
## # ℹ 134 more rows

saveRDS(glm_results, "out/dataframes/glm_results_df.rds")
writexl::write_xlsx(glm_results, "out/dataframes/glm_results_df.xlsx")
  

# Load data

glm_results <- readRDS("out/dataframes/glm_results_df.rds")

# Table of results
tbl_binary_glm <- make_stratified_table(
  glm_results,
  "**GLM Multilevel models**",
  "Prevalence Ratio (95% CI). All models include Region as a second-order covariate."
)

tbl_binary_glm

GLM Multilevel models
	Doctor			Nurse
	Crude1	Partially adjusted2	Fully adjusted3	Crude1	Partially adjusted2	Fully adjusted3
Depression
Bullying	2.22 (2.07, 2.39)	2.02 (1.88, 2.17)	2.02 (1.88, 2.18)	2.65 (2.44, 2.87)	2.49 (2.29, 2.70)	2.49 (2.30, 2.71)
Sexual Harassment	1.78 (1.61, 1.96)	1.48 (1.34, 1.64)	1.49 (1.34, 1.65)	2.38 (2.13, 2.66)	2.07 (1.85, 2.32)	2.08 (1.86, 2.33)
Violent threats	1.99 (1.85, 2.15)	1.84 (1.70, 1.99)	1.85 (1.71, 2.00)	2.28 (2.08, 2.51)	2.11 (1.92, 2.32)	2.12 (1.93, 2.34)
Physical violence	1.68 (1.44, 1.97)	1.49 (1.27, 1.75)	1.49 (1.27, 1.76)	2.23 (1.93, 2.59)	2.02 (1.74, 2.35)	2.02 (1.74, 2.36)
Colleague support	0.63 (0.59, 0.68)	0.66 (0.61, 0.71)	0.66 (0.61, 0.71)	0.58 (0.54, 0.63)	0.59 (0.55, 0.64)	0.59 (0.55, 0.64)
Support from superiors	0.58 (0.54, 0.63)	0.62 (0.57, 0.66)	0.62 (0.57, 0.67)	0.50 (0.46, 0.54)	0.52 (0.48, 0.56)	0.52 (0.48, 0.56)
Meaning in work	0.48 (0.43, 0.52)	0.50 (0.46, 0.55)	0.50 (0.46, 0.55)	0.42 (0.38, 0.46)	0.43 (0.39, 0.48)	0.44 (0.40, 0.48)
Purpose in work	0.45 (0.41, 0.50)	0.48 (0.43, 0.53)	0.48 (0.44, 0.53)	0.46 (0.41, 0.51)	0.48 (0.43, 0.54)	0.49 (0.44, 0.55)
Anxiety
Bullying	2.33 (2.14, 2.53)	2.09 (1.92, 2.28)	2.10 (1.92, 2.29)	2.77 (2.52, 3.04)	2.65 (2.41, 2.92)	2.66 (2.42, 2.93)
Sexual Harassment	1.97 (1.75, 2.20)	1.61 (1.43, 1.81)	1.62 (1.44, 1.82)	2.53 (2.23, 2.87)	2.26 (1.99, 2.58)	2.28 (2.00, 2.60)
Violent threats	2.06 (1.89, 2.26)	1.92 (1.75, 2.10)	1.93 (1.76, 2.12)	2.35 (2.10, 2.62)	2.25 (2.01, 2.52)	2.27 (2.03, 2.54)
Physical violence	1.96 (1.64, 2.33)	1.74 (1.45, 2.08)	1.79 (1.49, 2.15)	2.24 (1.89, 2.67)	2.11 (1.77, 2.52)	2.14 (1.80, 2.56)
Colleague support	0.62 (0.57, 0.68)	0.65 (0.60, 0.71)	0.65 (0.60, 0.71)	0.57 (0.52, 0.63)	0.58 (0.53, 0.63)	0.57 (0.52, 0.63)
Support from superiors	0.56 (0.52, 0.61)	0.60 (0.55, 0.65)	0.60 (0.54, 0.65)	0.50 (0.45, 0.55)	0.51 (0.47, 0.57)	0.51 (0.47, 0.57)
Meaning in work	0.47 (0.43, 0.53)	0.49 (0.44, 0.55)	0.50 (0.45, 0.55)	0.46 (0.41, 0.51)	0.47 (0.42, 0.52)	0.47 (0.42, 0.53)
Purpose in work	0.44 (0.39, 0.49)	0.46 (0.41, 0.52)	0.47 (0.41, 0.53)	0.49 (0.43, 0.55)	0.50 (0.44, 0.57)	0.51 (0.45, 0.58)
Suicide thoughts
Bullying	2.49 (2.21, 2.81)	2.22 (1.96, 2.51)	2.22 (1.96, 2.51)	3.39 (2.97, 3.86)	3.13 (2.74, 3.58)	3.11 (2.73, 3.56)
Sexual Harassment	2.61 (2.25, 3.02)	2.14 (1.84, 2.49)	2.13 (1.83, 2.48)	3.32 (2.82, 3.91)	2.81 (2.37, 3.32)	2.78 (2.35, 3.30)
Violent threats	2.49 (2.20, 2.82)	2.18 (1.92, 2.47)	2.18 (1.92, 2.48)	2.88 (2.49, 3.33)	2.59 (2.23, 3.01)	2.56 (2.20, 2.99)
Physical violence	2.85 (2.31, 3.52)	2.34 (1.89, 2.90)	2.37 (1.90, 2.95)	3.12 (2.52, 3.85)	2.69 (2.17, 3.34)	2.68 (2.16, 3.34)
Colleague support	0.60 (0.53, 0.68)	0.64 (0.56, 0.72)	0.64 (0.57, 0.72)	0.51 (0.45, 0.59)	0.52 (0.46, 0.60)	0.52 (0.46, 0.60)
Support from superiors	0.56 (0.50, 0.63)	0.61 (0.54, 0.69)	0.61 (0.54, 0.69)	0.53 (0.46, 0.61)	0.56 (0.49, 0.64)	0.55 (0.48, 0.63)
Meaning in work	0.40 (0.35, 0.47)	0.43 (0.37, 0.50)	0.44 (0.38, 0.51)	0.44 (0.38, 0.52)	0.47 (0.40, 0.55)	0.47 (0.40, 0.55)
Purpose in work	0.35 (0.30, 0.41)	0.38 (0.33, 0.45)	0.39 (0.33, 0.46)	0.41 (0.35, 0.49)	0.44 (0.37, 0.52)	0.44 (0.37, 0.52)
Prevalence Ratio (95% CI). All models include Region as a second-order covariate.
1 Region as second-order covariate only.
2 Adjusted by Gender, Age group and Proximity to combat areas and occupied territories .
3 Partially adjusted models plus specific covariates: Bullying and sexual harassment adjusted by Healthcare setting, Post-graduate training and Length of service. Violent threats and physical violence adjusted by Healthcare setting. Support from colleagues or superiors adjusted by Healthcare setting and Length of service. Finding meaning or purpose in job adjusted by Healthcare setting, Relationship status, Having children, and Length of service.

gtsave(tbl_binary_glm, "out/tables/glm_res_tbl.docx")


ls() |> 
  str_subset("^glm_") |> 
  rm(list = _)

rm(glm_se_ratios, make_stratified_table, run_crude_glm, run_glm_adj)

GLM Dose-Response models

analysis_plan_dose <- tribble(
  ~Exposure,     ~Covar_Key,
  "work_21", "peer",
  "work_22", "peer",
  "work_31", "viol",
  "work_32", "viol",
  "work_30", "har_bul",
  "work_33", "har_bul",
  "wb_wami_1", "meanpur",
  "wb_wami_2", "meanpur",
)

outcomes_map_dose <- tribble(
  ~Outcome,    ~Family,
  "phq_sc",    "gaussian",
  "gad_sc",    "gaussian",
  "mh_phq_9",  "gaussian",
)


# Define exposure-outcome combinations
tasks_dose <- expand_grid(
  out_row = outcomes_map_dose,
  exp_row = analysis_plan_dose,
) |> 
  tidyr::unpack(cols = c(out_row, exp_row))


dose_all <- pmap_dfr(list(
  tasks_dose$Outcome,
  tasks_dose$Family,
  tasks_dose$Exposure,
  tasks_dose$Covar_Key
  ), function(outcome, family, exposure, cov_key) {
  
  message(paste("Processing:", outcome, "&", exposure))

  
  res_adj <- run_glm_dose_emm(
    data = ds_ua,
    outcome_var = outcome,
    exposure_var = exposure,
    confounder_list = covariate_map[[cov_key]],
    model_label = "Fully adjusted"
  ) 
  
  print(res_adj)
})

## Processing: phq_sc & work_21

## Loading saved bundle: dose_dep_cols_fully_adjusted.rds

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 19420' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 19420)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 19420' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 19420)' or larger];
## but be warned that this may result in large computation time and memory use.

##   Outcome Exposure               Level  estimate conf.low conf.high
## 1  phq_sc  work_21 Never / Hardly ever 10.363076 9.847523 10.878629
## 2  phq_sc  work_21              Seldom  9.513735 9.097535  9.929935
## 3  phq_sc  work_21           Sometimes  7.946966 7.578833  8.315099
## 4  phq_sc  work_21               Often  7.246105 6.881685  7.610524
## 5  phq_sc  work_21              Always  5.526441 5.158099  5.894784
##            Model N_Analysis
## 1 Fully adjusted      19420
## 2 Fully adjusted      19420
## 3 Fully adjusted      19420
## 4 Fully adjusted      19420
## 5 Fully adjusted      19420

## Processing: phq_sc & work_22

## Loading saved bundle: dose_dep_sup_fully_adjusted.rds

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 19414' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 19414)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 19414' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 19414)' or larger];
## but be warned that this may result in large computation time and memory use.

##   Outcome Exposure               Level  estimate conf.low conf.high
## 1  phq_sc  work_22 Never / Hardly ever 10.388833 9.983641 10.794025
## 2  phq_sc  work_22              Seldom  9.170733 8.776369  9.565097
## 3  phq_sc  work_22           Sometimes  7.813671 7.450037  8.177305
## 4  phq_sc  work_22               Often  7.044911 6.680163  7.409660
## 5  phq_sc  work_22              Always  5.526779 5.168358  5.885201
##            Model N_Analysis
## 1 Fully adjusted      19414
## 2 Fully adjusted      19414
## 3 Fully adjusted      19414
## 4 Fully adjusted      19414
## 5 Fully adjusted      19414

## Processing: phq_sc & work_31

## Loading saved bundle: dose_dep_threats_fully_adjusted.rds

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 19462' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 19462)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 19462' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 19462)' or larger];
## but be warned that this may result in large computation time and memory use.

##   Outcome Exposure            Level  estimate  conf.low conf.high
## 1  phq_sc  work_31               No  6.493881  6.216748  6.771014
## 2  phq_sc  work_31 Yes, a few times  9.986213  9.648258 10.324168
## 3  phq_sc  work_31     Yes, monthly 11.865272 11.082108 12.648436
## 4  phq_sc  work_31      Yes, weekly 12.318530 11.433075 13.203986
## 5  phq_sc  work_31       Yes, daily 12.721124 11.766747 13.675501
##            Model N_Analysis
## 1 Fully adjusted      19462
## 2 Fully adjusted      19462
## 3 Fully adjusted      19462
## 4 Fully adjusted      19462
## 5 Fully adjusted      19462

## Processing: phq_sc & work_32

## Loading saved bundle: dose_dep_viol_fully_adjusted.rds

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 19465' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 19465)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 19465' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 19465)' or larger];
## but be warned that this may result in large computation time and memory use.

##   Outcome Exposure            Level  estimate  conf.low conf.high
## 1  phq_sc  work_32               No  7.125265  6.827682  7.422848
## 2  phq_sc  work_32 Yes, a few times 10.796591 10.233444 11.359738
## 3  phq_sc  work_32     Yes, monthly 12.159947 10.326065 13.993829
## 4  phq_sc  work_32      Yes, weekly 10.732886  8.946850 12.518921
## 5  phq_sc  work_32       Yes, daily 11.374434 10.045445 12.703423
##            Model N_Analysis
## 1 Fully adjusted      19465
## 2 Fully adjusted      19465
## 3 Fully adjusted      19465
## 4 Fully adjusted      19465
## 5 Fully adjusted      19465

## Processing: phq_sc & work_30

## Loading saved bundle: dose_dep_har_fully_adjusted.rds

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 19421' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 19421)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 19421' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 19421)' or larger];
## but be warned that this may result in large computation time and memory use.

##   Outcome Exposure            Level  estimate  conf.low conf.high
## 1  phq_sc  work_30               No  6.947317  6.611941  7.282692
## 2  phq_sc  work_30 Yes, a few times 10.653725 10.200519 11.106931
## 3  phq_sc  work_30     Yes, monthly 10.997003  9.857730 12.136275
## 4  phq_sc  work_30      Yes, weekly 10.844679  9.763083 11.926274
## 5  phq_sc  work_30       Yes, daily 11.562423 10.500516 12.624331
##            Model N_Analysis
## 1 Fully adjusted      19421
## 2 Fully adjusted      19421
## 3 Fully adjusted      19421
## 4 Fully adjusted      19421
## 5 Fully adjusted      19421

## Processing: phq_sc & work_33

## Loading saved bundle: dose_dep_bul_fully_adjusted.rds

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 19427' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 19427)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 19427' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 19427)' or larger];
## but be warned that this may result in large computation time and memory use.

##   Outcome Exposure            Level  estimate  conf.low conf.high
## 1  phq_sc  work_33               No  6.234596  5.924246  6.544945
## 2  phq_sc  work_33 Yes, a few times  9.978713  9.629255 10.328170
## 3  phq_sc  work_33     Yes, monthly 12.167895 11.539342 12.796447
## 4  phq_sc  work_33      Yes, weekly 13.128526 12.478244 13.778808
## 5  phq_sc  work_33       Yes, daily 14.121960 13.395403 14.848518
##            Model N_Analysis
## 1 Fully adjusted      19427
## 2 Fully adjusted      19427
## 3 Fully adjusted      19427
## 4 Fully adjusted      19427
## 5 Fully adjusted      19427

## Processing: phq_sc & wb_wami_1

## Loading saved bundle: dose_dep_mean_fully_adjusted.rds

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 19072' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 19072)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 19072' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 19072)' or larger];
## but be warned that this may result in large computation time and memory use.

##   Outcome  Exposure                   Level  estimate  conf.low conf.high
## 1  phq_sc wb_wami_1       Absolutely untrue 10.628644 10.160661 11.096627
## 2  phq_sc wb_wami_1           Mostly untrue  9.912557  9.512552 10.312561
## 3  phq_sc wb_wami_1 Neither true nor untrue  9.145213  8.779508  9.510918
## 4  phq_sc wb_wami_1             Mostly true  7.107908  6.765879  7.449937
## 5  phq_sc wb_wami_1         Absolutely true  5.393218  5.045072  5.741364
##            Model N_Analysis
## 1 Fully adjusted      19072
## 2 Fully adjusted      19072
## 3 Fully adjusted      19072
## 4 Fully adjusted      19072
## 5 Fully adjusted      19072

## Processing: phq_sc & wb_wami_2

## Loading saved bundle: dose_dep_purp_fully_adjusted.rds

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 19067' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 19067)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 19067' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 19067)' or larger];
## but be warned that this may result in large computation time and memory use.

##   Outcome  Exposure                   Level  estimate  conf.low conf.high
## 1  phq_sc wb_wami_2       Absolutely untrue 12.381539 11.805751 12.957328
## 2  phq_sc wb_wami_2           Mostly untrue 10.021557  9.587946 10.455168
## 3  phq_sc wb_wami_2 Neither true nor untrue  9.509440  9.128101  9.890778
## 4  phq_sc wb_wami_2             Mostly true  7.501071  7.163322  7.838821
## 5  phq_sc wb_wami_2         Absolutely true  6.022585  5.687151  6.358019
##            Model N_Analysis
## 1 Fully adjusted      19067
## 2 Fully adjusted      19067
## 3 Fully adjusted      19067
## 4 Fully adjusted      19067
## 5 Fully adjusted      19067

## Processing: gad_sc & work_21

## Loading saved bundle: dose_anx_cols_fully_adjusted.rds

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 19327' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 19327)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 19327' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 19327)' or larger];
## but be warned that this may result in large computation time and memory use.

##   Outcome Exposure               Level estimate conf.low conf.high
## 1  gad_sc  work_21 Never / Hardly ever 8.091918 7.636567  8.547269
## 2  gad_sc  work_21              Seldom 7.496980 7.132635  7.861326
## 3  gad_sc  work_21           Sometimes 6.219781 5.900879  6.538683
## 4  gad_sc  work_21               Often 5.785153 5.469945  6.100361
## 5  gad_sc  work_21              Always 4.479655 4.160506  4.798804
##            Model N_Analysis
## 1 Fully adjusted      19327
## 2 Fully adjusted      19327
## 3 Fully adjusted      19327
## 4 Fully adjusted      19327
## 5 Fully adjusted      19327

## Processing: gad_sc & work_22

## Loading saved bundle: dose_anx_sup_fully_adjusted.rds

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 19319' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 19319)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 19319' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 19319)' or larger];
## but be warned that this may result in large computation time and memory use.

##   Outcome Exposure               Level estimate conf.low conf.high
## 1  gad_sc  work_22 Never / Hardly ever 8.132103 7.777041  8.487165
## 2  gad_sc  work_22              Seldom 7.237756 6.892799  7.582712
## 3  gad_sc  work_22           Sometimes 6.148538 5.832717  6.464359
## 4  gad_sc  work_22               Often 5.587018 5.270220  5.903815
## 5  gad_sc  work_22              Always 4.497388 4.186502  4.808274
##            Model N_Analysis
## 1 Fully adjusted      19319
## 2 Fully adjusted      19319
## 3 Fully adjusted      19319
## 4 Fully adjusted      19319
## 5 Fully adjusted      19319

## Processing: gad_sc & work_31

## Loading saved bundle: dose_anx_threats_fully_adjusted.rds

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 19367' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 19367)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 19367' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 19367)' or larger];
## but be warned that this may result in large computation time and memory use.

##   Outcome Exposure            Level  estimate conf.low conf.high          Model
## 1  gad_sc  work_31               No  5.248825 5.010636  5.487014 Fully adjusted
## 2  gad_sc  work_31 Yes, a few times  8.020915 7.725539  8.316291 Fully adjusted
## 3  gad_sc  work_31     Yes, monthly  9.525161 8.822561 10.227761 Fully adjusted
## 4  gad_sc  work_31      Yes, weekly  9.638510 8.838575 10.438445 Fully adjusted
## 5  gad_sc  work_31       Yes, daily 10.152591 9.291730 11.013451 Fully adjusted
##   N_Analysis
## 1      19367
## 2      19367
## 3      19367
## 4      19367
## 5      19367

## Processing: gad_sc & work_32

## Loading saved bundle: dose_anx_viol_fully_adjusted.rds

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 19366' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 19366)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 19366' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 19366)' or larger];
## but be warned that this may result in large computation time and memory use.

##   Outcome Exposure            Level estimate conf.low conf.high          Model
## 1  gad_sc  work_32               No 5.748004 5.493590  6.002418 Fully adjusted
## 2  gad_sc  work_32 Yes, a few times 8.625767 8.126863  9.124670 Fully adjusted
## 3  gad_sc  work_32     Yes, monthly 9.852536 8.185995 11.519078 Fully adjusted
## 4  gad_sc  work_32      Yes, weekly 8.593344 6.992030 10.194657 Fully adjusted
## 5  gad_sc  work_32       Yes, daily 9.065210 7.874921 10.255498 Fully adjusted
##   N_Analysis
## 1      19366
## 2      19366
## 3      19366
## 4      19366
## 5      19366

## Processing: gad_sc & work_30

## Loading saved bundle: dose_anx_har_fully_adjusted.rds

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 19325' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 19325)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 19325' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 19325)' or larger];
## but be warned that this may result in large computation time and memory use.

##   Outcome Exposure            Level estimate conf.low conf.high          Model
## 1  gad_sc  work_30               No 5.522418 5.231924  5.812911 Fully adjusted
## 2  gad_sc  work_30 Yes, a few times 8.468788 8.069171  8.868404 Fully adjusted
## 3  gad_sc  work_30     Yes, monthly 8.675785 7.658583  9.692988 Fully adjusted
## 4  gad_sc  work_30      Yes, weekly 8.396146 7.422224  9.370068 Fully adjusted
## 5  gad_sc  work_30       Yes, daily 8.868974 7.913200  9.824749 Fully adjusted
##   N_Analysis
## 1      19325
## 2      19325
## 3      19325
## 4      19325
## 5      19325

## Processing: gad_sc & work_33

## Loading saved bundle: dose_anx_bul_fully_adjusted.rds

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 19332' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 19332)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 19332' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 19332)' or larger];
## but be warned that this may result in large computation time and memory use.

##   Outcome Exposure            Level  estimate  conf.low conf.high
## 1  gad_sc  work_33               No  4.936351  4.665899  5.206802
## 2  gad_sc  work_33 Yes, a few times  7.919563  7.612349  8.226778
## 3  gad_sc  work_33     Yes, monthly  9.895488  9.330097 10.460879
## 4  gad_sc  work_33      Yes, weekly 10.360943  9.773168 10.948717
## 5  gad_sc  work_33       Yes, daily 11.385672 10.732312 12.039032
##            Model N_Analysis
## 1 Fully adjusted      19332
## 2 Fully adjusted      19332
## 3 Fully adjusted      19332
## 4 Fully adjusted      19332
## 5 Fully adjusted      19332

## Processing: gad_sc & wb_wami_1

## Loading saved bundle: dose_anx_mean_fully_adjusted.rds

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 19068' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 19068)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 19068' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 19068)' or larger];
## but be warned that this may result in large computation time and memory use.

##   Outcome  Exposure                   Level estimate conf.low conf.high
## 1  gad_sc wb_wami_1       Absolutely untrue 8.024623 7.606465  8.442782
## 2  gad_sc wb_wami_1           Mostly untrue 7.648236 7.293441  8.003032
## 3  gad_sc wb_wami_1 Neither true nor untrue 7.086709 6.764056  7.409363
## 4  gad_sc wb_wami_1             Mostly true 5.629727 5.329523  5.929931
## 5  gad_sc wb_wami_1         Absolutely true 4.415986 4.110003  4.721969
##            Model N_Analysis
## 1 Fully adjusted      19068
## 2 Fully adjusted      19068
## 3 Fully adjusted      19068
## 4 Fully adjusted      19068
## 5 Fully adjusted      19068

## Processing: gad_sc & wb_wami_2

## Loading saved bundle: dose_anx_purp_fully_adjusted.rds

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 19064' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 19064)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 19064' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 19064)' or larger];
## but be warned that this may result in large computation time and memory use.

##   Outcome  Exposure                   Level estimate conf.low conf.high
## 1  gad_sc wb_wami_2       Absolutely untrue 9.244481 8.726893  9.762068
## 2  gad_sc wb_wami_2           Mostly untrue 7.730779 7.343966  8.117593
## 3  gad_sc wb_wami_2 Neither true nor untrue 7.380740 7.042218  7.719262
## 4  gad_sc wb_wami_2             Mostly true 5.925516 5.627843  6.223190
## 5  gad_sc wb_wami_2         Absolutely true 4.815917 4.520348  5.111485
##            Model N_Analysis
## 1 Fully adjusted      19064
## 2 Fully adjusted      19064
## 3 Fully adjusted      19064
## 4 Fully adjusted      19064
## 5 Fully adjusted      19064

## Processing: mh_phq_9 & work_21

## Loading saved bundle: dose_suic_cols_fully_adjusted.rds

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 19508' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 19508)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 19508' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 19508)' or larger];
## but be warned that this may result in large computation time and memory use.

##    Outcome Exposure               Level  estimate  conf.low conf.high
## 1 mh_phq_9  work_21 Never / Hardly ever 0.4261153 0.3875278 0.4647027
## 2 mh_phq_9  work_21              Seldom 0.2856393 0.2562579 0.3150207
## 3 mh_phq_9  work_21           Sometimes 0.2219403 0.1973807 0.2464999
## 4 mh_phq_9  work_21               Often 0.1911216 0.1669513 0.2152919
## 5 mh_phq_9  work_21              Always 0.1479667 0.1234016 0.1725319
##            Model N_Analysis
## 1 Fully adjusted      19508
## 2 Fully adjusted      19508
## 3 Fully adjusted      19508
## 4 Fully adjusted      19508
## 5 Fully adjusted      19508

## Processing: mh_phq_9 & work_22

## Loading saved bundle: dose_suic_sup_fully_adjusted.rds

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 19499' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 19499)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 19499' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 19499)' or larger];
## but be warned that this may result in large computation time and memory use.

##    Outcome Exposure               Level  estimate  conf.low conf.high
## 1 mh_phq_9  work_22 Never / Hardly ever 0.3335940 0.3048539 0.3623340
## 2 mh_phq_9  work_22              Seldom 0.2724634 0.2448177 0.3001092
## 3 mh_phq_9  work_22           Sometimes 0.2096436 0.1850993 0.2341879
## 4 mh_phq_9  work_22               Often 0.1841638 0.1595177 0.2088099
## 5 mh_phq_9  work_22              Always 0.1591480 0.1351875 0.1831086
##            Model N_Analysis
## 1 Fully adjusted      19499
## 2 Fully adjusted      19499
## 3 Fully adjusted      19499
## 4 Fully adjusted      19499
## 5 Fully adjusted      19499

## Processing: mh_phq_9 & work_31

## Loading saved bundle: dose_suic_threats_fully_adjusted.rds

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 19550' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 19550)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 19550' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 19550)' or larger];
## but be warned that this may result in large computation time and memory use.

##    Outcome Exposure            Level  estimate  conf.low conf.high
## 1 mh_phq_9  work_31               No 0.1326168 0.1154523 0.1497812
## 2 mh_phq_9  work_31 Yes, a few times 0.2801490 0.2567130 0.3035850
## 3 mh_phq_9  work_31     Yes, monthly 0.3935564 0.3307887 0.4563241
## 4 mh_phq_9  work_31      Yes, weekly 0.6008730 0.5293428 0.6724031
## 5 mh_phq_9  work_31       Yes, daily 0.6616636 0.5849075 0.7384196
##            Model N_Analysis
## 1 Fully adjusted      19550
## 2 Fully adjusted      19550
## 3 Fully adjusted      19550
## 4 Fully adjusted      19550
## 5 Fully adjusted      19550

## Processing: mh_phq_9 & work_32

## Loading saved bundle: dose_suic_viol_fully_adjusted.rds

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 19553' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 19553)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 19553' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 19553)' or larger];
## but be warned that this may result in large computation time and memory use.

##    Outcome Exposure            Level  estimate  conf.low conf.high
## 1 mh_phq_9  work_32               No 0.1594294 0.1417022 0.1771567
## 2 mh_phq_9  work_32 Yes, a few times 0.3630296 0.3204588 0.4056003
## 3 mh_phq_9  work_32     Yes, monthly 0.5540771 0.4059895 0.7021647
## 4 mh_phq_9  work_32      Yes, weekly 0.7255865 0.5832126 0.8679604
## 5 mh_phq_9  work_32       Yes, daily 0.9496561 0.8436514 1.0556608
##            Model N_Analysis
## 1 Fully adjusted      19553
## 2 Fully adjusted      19553
## 3 Fully adjusted      19553
## 4 Fully adjusted      19553
## 5 Fully adjusted      19553

## Processing: mh_phq_9 & work_30

## Loading saved bundle: dose_suic_har_fully_adjusted.rds

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 19508' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 19508)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 19508' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 19508)' or larger];
## but be warned that this may result in large computation time and memory use.

##    Outcome Exposure            Level  estimate  conf.low conf.high
## 1 mh_phq_9  work_30               No 0.1777764 0.1559215 0.1996314
## 2 mh_phq_9  work_30 Yes, a few times 0.3814765 0.3483884 0.4145646
## 3 mh_phq_9  work_30     Yes, monthly 0.4472742 0.3560333 0.5385150
## 4 mh_phq_9  work_30      Yes, weekly 0.4919372 0.4054388 0.5784356
## 5 mh_phq_9  work_30       Yes, daily 0.7711869 0.6862830 0.8560908
##            Model N_Analysis
## 1 Fully adjusted      19508
## 2 Fully adjusted      19508
## 3 Fully adjusted      19508
## 4 Fully adjusted      19508
## 5 Fully adjusted      19508

## Processing: mh_phq_9 & work_33

## Loading saved bundle: dose_suic_bul_fully_adjusted.rds

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 19512' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 19512)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 19512' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 19512)' or larger];
## but be warned that this may result in large computation time and memory use.

##    Outcome Exposure            Level  estimate  conf.low conf.high
## 1 mh_phq_9  work_33               No 0.1574605 0.1364212 0.1784998
## 2 mh_phq_9  work_33 Yes, a few times 0.2864191 0.2613921 0.3114460
## 3 mh_phq_9  work_33     Yes, monthly 0.4757194 0.4248866 0.5265521
## 4 mh_phq_9  work_33      Yes, weekly 0.4917597 0.4390195 0.5445000
## 5 mh_phq_9  work_33       Yes, daily 0.6779867 0.6187704 0.7372029
##            Model N_Analysis
## 1 Fully adjusted      19512
## 2 Fully adjusted      19512
## 3 Fully adjusted      19512
## 4 Fully adjusted      19512
## 5 Fully adjusted      19512

## Processing: mh_phq_9 & wb_wami_1

## Loading saved bundle: dose_suic_mean_fully_adjusted.rds

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 19145' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 19145)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 19145' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 19145)' or larger];
## but be warned that this may result in large computation time and memory use.

##    Outcome  Exposure                   Level  estimate  conf.low conf.high
## 1 mh_phq_9 wb_wami_1       Absolutely untrue 0.3921259 0.3568040 0.4274478
## 2 mh_phq_9 wb_wami_1           Mostly untrue 0.2893525 0.2604499 0.3182551
## 3 mh_phq_9 wb_wami_1 Neither true nor untrue 0.2608666 0.2353646 0.2863687
## 4 mh_phq_9 wb_wami_1             Mostly true 0.1876282 0.1645425 0.2107139
## 5 mh_phq_9 wb_wami_1         Absolutely true 0.1544227 0.1306970 0.1781483
##            Model N_Analysis
## 1 Fully adjusted      19145
## 2 Fully adjusted      19145
## 3 Fully adjusted      19145
## 4 Fully adjusted      19145
## 5 Fully adjusted      19145

## Processing: mh_phq_9 & wb_wami_2

## Loading saved bundle: dose_suic_purp_fully_adjusted.rds

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 19140' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 19140)' or larger];
## but be warned that this may result in large computation time and memory use.

## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 19140' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 19140)' or larger];
## but be warned that this may result in large computation time and memory use.

##    Outcome  Exposure                   Level  estimate  conf.low conf.high
## 1 mh_phq_9 wb_wami_2       Absolutely untrue 0.5157833 0.4708218 0.5607447
## 2 mh_phq_9 wb_wami_2           Mostly untrue 0.3230118 0.2908764 0.3551472
## 3 mh_phq_9 wb_wami_2 Neither true nor untrue 0.2897936 0.2625895 0.3169977
## 4 mh_phq_9 wb_wami_2             Mostly true 0.2026302 0.1797418 0.2255185
## 5 mh_phq_9 wb_wami_2         Absolutely true 0.1592481 0.1365766 0.1819195
##            Model N_Analysis
## 1 Fully adjusted      19140
## 2 Fully adjusted      19140
## 3 Fully adjusted      19140
## 4 Fully adjusted      19140
## 5 Fully adjusted      19140

dose_all <- 
  dose_all |> 
  mutate(
    Est_CI = sprintf("%.2f (%.2f, %.2f)", estimate, conf.low, conf.high),
    Exposure = case_when(
      Exposure == "work_21" ~ "Colleague support",
      Exposure == "work_22" ~ "Support from superiors",
      Exposure == "work_31" ~ "Violent threats",
      Exposure == "work_32" ~ "Physical violence",
      Exposure == "work_30" ~ "Sexual Harassment",
      Exposure == "work_33" ~ "Bullying",
      Exposure == "wb_wami_1" ~ "Meaning in work",
      Exposure == "wb_wami_2" ~ "Purpose in work",
      TRUE ~ Exposure),
    Outcome = case_when(
      Outcome == "phq_sc" ~ "PHQ-9 score",
      Outcome == "gad_sc" ~ "GAD-7 score",
      Outcome == "mh_phq_9" ~ "PHQ-9 suicide item score",
      TRUE ~ Outcome
    ),
    Category = if_else(
      Exposure %in% c("Violent threats", "Physical violence",
                      "Sexual Harassment", "Bullying"),
      "Risk factor",
      "Protective factor")
    )

dose_all

##                      Outcome               Exposure                   Level
## 1                PHQ-9 score      Colleague support     Never / Hardly ever
## 2                PHQ-9 score      Colleague support                  Seldom
## 3                PHQ-9 score      Colleague support               Sometimes
## 4                PHQ-9 score      Colleague support                   Often
## 5                PHQ-9 score      Colleague support                  Always
## 6                PHQ-9 score Support from superiors     Never / Hardly ever
## 7                PHQ-9 score Support from superiors                  Seldom
## 8                PHQ-9 score Support from superiors               Sometimes
## 9                PHQ-9 score Support from superiors                   Often
## 10               PHQ-9 score Support from superiors                  Always
## 11               PHQ-9 score        Violent threats                      No
## 12               PHQ-9 score        Violent threats        Yes, a few times
## 13               PHQ-9 score        Violent threats            Yes, monthly
## 14               PHQ-9 score        Violent threats             Yes, weekly
## 15               PHQ-9 score        Violent threats              Yes, daily
## 16               PHQ-9 score      Physical violence                      No
## 17               PHQ-9 score      Physical violence        Yes, a few times
## 18               PHQ-9 score      Physical violence            Yes, monthly
## 19               PHQ-9 score      Physical violence             Yes, weekly
## 20               PHQ-9 score      Physical violence              Yes, daily
## 21               PHQ-9 score      Sexual Harassment                      No
## 22               PHQ-9 score      Sexual Harassment        Yes, a few times
## 23               PHQ-9 score      Sexual Harassment            Yes, monthly
## 24               PHQ-9 score      Sexual Harassment             Yes, weekly
## 25               PHQ-9 score      Sexual Harassment              Yes, daily
## 26               PHQ-9 score               Bullying                      No
## 27               PHQ-9 score               Bullying        Yes, a few times
## 28               PHQ-9 score               Bullying            Yes, monthly
## 29               PHQ-9 score               Bullying             Yes, weekly
## 30               PHQ-9 score               Bullying              Yes, daily
## 31               PHQ-9 score        Meaning in work       Absolutely untrue
## 32               PHQ-9 score        Meaning in work           Mostly untrue
## 33               PHQ-9 score        Meaning in work Neither true nor untrue
## 34               PHQ-9 score        Meaning in work             Mostly true
## 35               PHQ-9 score        Meaning in work         Absolutely true
## 36               PHQ-9 score        Purpose in work       Absolutely untrue
## 37               PHQ-9 score        Purpose in work           Mostly untrue
## 38               PHQ-9 score        Purpose in work Neither true nor untrue
## 39               PHQ-9 score        Purpose in work             Mostly true
## 40               PHQ-9 score        Purpose in work         Absolutely true
## 41               GAD-7 score      Colleague support     Never / Hardly ever
## 42               GAD-7 score      Colleague support                  Seldom
## 43               GAD-7 score      Colleague support               Sometimes
## 44               GAD-7 score      Colleague support                   Often
## 45               GAD-7 score      Colleague support                  Always
## 46               GAD-7 score Support from superiors     Never / Hardly ever
## 47               GAD-7 score Support from superiors                  Seldom
## 48               GAD-7 score Support from superiors               Sometimes
## 49               GAD-7 score Support from superiors                   Often
## 50               GAD-7 score Support from superiors                  Always
## 51               GAD-7 score        Violent threats                      No
## 52               GAD-7 score        Violent threats        Yes, a few times
## 53               GAD-7 score        Violent threats            Yes, monthly
## 54               GAD-7 score        Violent threats             Yes, weekly
## 55               GAD-7 score        Violent threats              Yes, daily
## 56               GAD-7 score      Physical violence                      No
## 57               GAD-7 score      Physical violence        Yes, a few times
## 58               GAD-7 score      Physical violence            Yes, monthly
## 59               GAD-7 score      Physical violence             Yes, weekly
## 60               GAD-7 score      Physical violence              Yes, daily
## 61               GAD-7 score      Sexual Harassment                      No
## 62               GAD-7 score      Sexual Harassment        Yes, a few times
## 63               GAD-7 score      Sexual Harassment            Yes, monthly
## 64               GAD-7 score      Sexual Harassment             Yes, weekly
## 65               GAD-7 score      Sexual Harassment              Yes, daily
## 66               GAD-7 score               Bullying                      No
## 67               GAD-7 score               Bullying        Yes, a few times
## 68               GAD-7 score               Bullying            Yes, monthly
## 69               GAD-7 score               Bullying             Yes, weekly
## 70               GAD-7 score               Bullying              Yes, daily
## 71               GAD-7 score        Meaning in work       Absolutely untrue
## 72               GAD-7 score        Meaning in work           Mostly untrue
## 73               GAD-7 score        Meaning in work Neither true nor untrue
## 74               GAD-7 score        Meaning in work             Mostly true
## 75               GAD-7 score        Meaning in work         Absolutely true
## 76               GAD-7 score        Purpose in work       Absolutely untrue
## 77               GAD-7 score        Purpose in work           Mostly untrue
## 78               GAD-7 score        Purpose in work Neither true nor untrue
## 79               GAD-7 score        Purpose in work             Mostly true
## 80               GAD-7 score        Purpose in work         Absolutely true
## 81  PHQ-9 suicide item score      Colleague support     Never / Hardly ever
## 82  PHQ-9 suicide item score      Colleague support                  Seldom
## 83  PHQ-9 suicide item score      Colleague support               Sometimes
## 84  PHQ-9 suicide item score      Colleague support                   Often
## 85  PHQ-9 suicide item score      Colleague support                  Always
## 86  PHQ-9 suicide item score Support from superiors     Never / Hardly ever
## 87  PHQ-9 suicide item score Support from superiors                  Seldom
## 88  PHQ-9 suicide item score Support from superiors               Sometimes
## 89  PHQ-9 suicide item score Support from superiors                   Often
## 90  PHQ-9 suicide item score Support from superiors                  Always
## 91  PHQ-9 suicide item score        Violent threats                      No
## 92  PHQ-9 suicide item score        Violent threats        Yes, a few times
## 93  PHQ-9 suicide item score        Violent threats            Yes, monthly
## 94  PHQ-9 suicide item score        Violent threats             Yes, weekly
## 95  PHQ-9 suicide item score        Violent threats              Yes, daily
## 96  PHQ-9 suicide item score      Physical violence                      No
## 97  PHQ-9 suicide item score      Physical violence        Yes, a few times
## 98  PHQ-9 suicide item score      Physical violence            Yes, monthly
## 99  PHQ-9 suicide item score      Physical violence             Yes, weekly
## 100 PHQ-9 suicide item score      Physical violence              Yes, daily
## 101 PHQ-9 suicide item score      Sexual Harassment                      No
## 102 PHQ-9 suicide item score      Sexual Harassment        Yes, a few times
## 103 PHQ-9 suicide item score      Sexual Harassment            Yes, monthly
## 104 PHQ-9 suicide item score      Sexual Harassment             Yes, weekly
## 105 PHQ-9 suicide item score      Sexual Harassment              Yes, daily
## 106 PHQ-9 suicide item score               Bullying                      No
## 107 PHQ-9 suicide item score               Bullying        Yes, a few times
## 108 PHQ-9 suicide item score               Bullying            Yes, monthly
## 109 PHQ-9 suicide item score               Bullying             Yes, weekly
## 110 PHQ-9 suicide item score               Bullying              Yes, daily
## 111 PHQ-9 suicide item score        Meaning in work       Absolutely untrue
## 112 PHQ-9 suicide item score        Meaning in work           Mostly untrue
## 113 PHQ-9 suicide item score        Meaning in work Neither true nor untrue
## 114 PHQ-9 suicide item score        Meaning in work             Mostly true
## 115 PHQ-9 suicide item score        Meaning in work         Absolutely true
## 116 PHQ-9 suicide item score        Purpose in work       Absolutely untrue
## 117 PHQ-9 suicide item score        Purpose in work           Mostly untrue
## 118 PHQ-9 suicide item score        Purpose in work Neither true nor untrue
## 119 PHQ-9 suicide item score        Purpose in work             Mostly true
## 120 PHQ-9 suicide item score        Purpose in work         Absolutely true
##       estimate   conf.low  conf.high          Model N_Analysis
## 1   10.3630761  9.8475228 10.8786295 Fully adjusted      19420
## 2    9.5137350  9.0975349  9.9299352 Fully adjusted      19420
## 3    7.9469661  7.5788334  8.3150987 Fully adjusted      19420
## 4    7.2461047  6.8816852  7.6105242 Fully adjusted      19420
## 5    5.5264413  5.1580987  5.8947839 Fully adjusted      19420
## 6   10.3888333  9.9836414 10.7940251 Fully adjusted      19414
## 7    9.1707329  8.7763692  9.5650966 Fully adjusted      19414
## 8    7.8136708  7.4500366  8.1773050 Fully adjusted      19414
## 9    7.0449115  6.6801633  7.4096596 Fully adjusted      19414
## 10   5.5267791  5.1683576  5.8852006 Fully adjusted      19414
## 11   6.4938806  6.2167477  6.7710135 Fully adjusted      19462
## 12   9.9862128  9.6482579 10.3241677 Fully adjusted      19462
## 13  11.8652719 11.0821075 12.6484363 Fully adjusted      19462
## 14  12.3185301 11.4330746 13.2039857 Fully adjusted      19462
## 15  12.7211240 11.7667466 13.6755015 Fully adjusted      19462
## 16   7.1252651  6.8276817  7.4228484 Fully adjusted      19465
## 17  10.7965910 10.2334437 11.3597384 Fully adjusted      19465
## 18  12.1599468 10.3260649 13.9938287 Fully adjusted      19465
## 19  10.7328857  8.9468503 12.5189210 Fully adjusted      19465
## 20  11.3744341 10.0454454 12.7034229 Fully adjusted      19465
## 21   6.9473170  6.6119415  7.2826924 Fully adjusted      19421
## 22  10.6537254 10.2005193 11.1069315 Fully adjusted      19421
## 23  10.9970029  9.8577304 12.1362754 Fully adjusted      19421
## 24  10.8446788  9.7630830 11.9262745 Fully adjusted      19421
## 25  11.5624233 10.5005158 12.6243309 Fully adjusted      19421
## 26   6.2345956  5.9242462  6.5449449 Fully adjusted      19427
## 27   9.9787128  9.6292551 10.3281705 Fully adjusted      19427
## 28  12.1678946 11.5393422 12.7964469 Fully adjusted      19427
## 29  13.1285263 12.4782444 13.7788082 Fully adjusted      19427
## 30  14.1219605 13.3954029 14.8485181 Fully adjusted      19427
## 31  10.6286439 10.1606612 11.0966266 Fully adjusted      19072
## 32   9.9125568  9.5125522 10.3125614 Fully adjusted      19072
## 33   9.1452130  8.7795083  9.5109178 Fully adjusted      19072
## 34   7.1079080  6.7658790  7.4499371 Fully adjusted      19072
## 35   5.3932181  5.0450719  5.7413643 Fully adjusted      19072
## 36  12.3815394 11.8057507 12.9573281 Fully adjusted      19067
## 37  10.0215567  9.5879456 10.4551678 Fully adjusted      19067
## 38   9.5094395  9.1281012  9.8907779 Fully adjusted      19067
## 39   7.5010715  7.1633222  7.8388207 Fully adjusted      19067
## 40   6.0225853  5.6871514  6.3580193 Fully adjusted      19067
## 41   8.0919178  7.6365671  8.5472685 Fully adjusted      19327
## 42   7.4969805  7.1326346  7.8613263 Fully adjusted      19327
## 43   6.2197806  5.9008787  6.5386825 Fully adjusted      19327
## 44   5.7851532  5.4699449  6.1003615 Fully adjusted      19327
## 45   4.4796553  4.1605064  4.7988042 Fully adjusted      19327
## 46   8.1321034  7.7770413  8.4871655 Fully adjusted      19319
## 47   7.2377555  6.8927994  7.5827117 Fully adjusted      19319
## 48   6.1485376  5.8327167  6.4643585 Fully adjusted      19319
## 49   5.5870179  5.2702203  5.9038155 Fully adjusted      19319
## 50   4.4973879  4.1865018  4.8082741 Fully adjusted      19319
## 51   5.2488248  5.0106357  5.4870138 Fully adjusted      19367
## 52   8.0209150  7.7255394  8.3162905 Fully adjusted      19367
## 53   9.5251608  8.8225605 10.2277610 Fully adjusted      19367
## 54   9.6385101  8.8385755 10.4384448 Fully adjusted      19367
## 55  10.1525908  9.2917303 11.0134513 Fully adjusted      19367
## 56   5.7480041  5.4935905  6.0024178 Fully adjusted      19366
## 57   8.6257665  8.1268634  9.1246696 Fully adjusted      19366
## 58   9.8525365  8.1859949 11.5190781 Fully adjusted      19366
## 59   8.5933435  6.9920303 10.1946567 Fully adjusted      19366
## 60   9.0652096  7.8749208 10.2554985 Fully adjusted      19366
## 61   5.5224179  5.2319244  5.8129113 Fully adjusted      19325
## 62   8.4687875  8.0691711  8.8684039 Fully adjusted      19325
## 63   8.6757854  7.6585827  9.6929881 Fully adjusted      19325
## 64   8.3961463  7.4222242  9.3700684 Fully adjusted      19325
## 65   8.8689745  7.9132004  9.8247485 Fully adjusted      19325
## 66   4.9363508  4.6658993  5.2068023 Fully adjusted      19332
## 67   7.9195634  7.6123489  8.2267778 Fully adjusted      19332
## 68   9.8954876  9.3300966 10.4608785 Fully adjusted      19332
## 69  10.3609425  9.7731682 10.9487168 Fully adjusted      19332
## 70  11.3856719 10.7323121 12.0390316 Fully adjusted      19332
## 71   8.0246232  7.6064649  8.4427816 Fully adjusted      19068
## 72   7.6482362  7.2934407  8.0030317 Fully adjusted      19068
## 73   7.0867094  6.7640557  7.4093631 Fully adjusted      19068
## 74   5.6297270  5.3295234  5.9299306 Fully adjusted      19068
## 75   4.4159862  4.1100034  4.7219689 Fully adjusted      19068
## 76   9.2444806  8.7268930  9.7620682 Fully adjusted      19064
## 77   7.7307795  7.3439659  8.1175931 Fully adjusted      19064
## 78   7.3807404  7.0422184  7.7192625 Fully adjusted      19064
## 79   5.9255163  5.6278428  6.2231897 Fully adjusted      19064
## 80   4.8159166  4.5203480  5.1114852 Fully adjusted      19064
## 81   0.4261153  0.3875278  0.4647027 Fully adjusted      19508
## 82   0.2856393  0.2562579  0.3150207 Fully adjusted      19508
## 83   0.2219403  0.1973807  0.2464999 Fully adjusted      19508
## 84   0.1911216  0.1669513  0.2152919 Fully adjusted      19508
## 85   0.1479667  0.1234016  0.1725319 Fully adjusted      19508
## 86   0.3335940  0.3048539  0.3623340 Fully adjusted      19499
## 87   0.2724634  0.2448177  0.3001092 Fully adjusted      19499
## 88   0.2096436  0.1850993  0.2341879 Fully adjusted      19499
## 89   0.1841638  0.1595177  0.2088099 Fully adjusted      19499
## 90   0.1591480  0.1351875  0.1831086 Fully adjusted      19499
## 91   0.1326168  0.1154523  0.1497812 Fully adjusted      19550
## 92   0.2801490  0.2567130  0.3035850 Fully adjusted      19550
## 93   0.3935564  0.3307887  0.4563241 Fully adjusted      19550
## 94   0.6008730  0.5293428  0.6724031 Fully adjusted      19550
## 95   0.6616636  0.5849075  0.7384196 Fully adjusted      19550
## 96   0.1594294  0.1417022  0.1771567 Fully adjusted      19553
## 97   0.3630296  0.3204588  0.4056003 Fully adjusted      19553
## 98   0.5540771  0.4059895  0.7021647 Fully adjusted      19553
## 99   0.7255865  0.5832126  0.8679604 Fully adjusted      19553
## 100  0.9496561  0.8436514  1.0556608 Fully adjusted      19553
## 101  0.1777764  0.1559215  0.1996314 Fully adjusted      19508
## 102  0.3814765  0.3483884  0.4145646 Fully adjusted      19508
## 103  0.4472742  0.3560333  0.5385150 Fully adjusted      19508
## 104  0.4919372  0.4054388  0.5784356 Fully adjusted      19508
## 105  0.7711869  0.6862830  0.8560908 Fully adjusted      19508
## 106  0.1574605  0.1364212  0.1784998 Fully adjusted      19512
## 107  0.2864191  0.2613921  0.3114460 Fully adjusted      19512
## 108  0.4757194  0.4248866  0.5265521 Fully adjusted      19512
## 109  0.4917597  0.4390195  0.5445000 Fully adjusted      19512
## 110  0.6779867  0.6187704  0.7372029 Fully adjusted      19512
## 111  0.3921259  0.3568040  0.4274478 Fully adjusted      19145
## 112  0.2893525  0.2604499  0.3182551 Fully adjusted      19145
## 113  0.2608666  0.2353646  0.2863687 Fully adjusted      19145
## 114  0.1876282  0.1645425  0.2107139 Fully adjusted      19145
## 115  0.1544227  0.1306970  0.1781483 Fully adjusted      19145
## 116  0.5157833  0.4708218  0.5607447 Fully adjusted      19140
## 117  0.3230118  0.2908764  0.3551472 Fully adjusted      19140
## 118  0.2897936  0.2625895  0.3169977 Fully adjusted      19140
## 119  0.2026302  0.1797418  0.2255185 Fully adjusted      19140
## 120  0.1592481  0.1365766  0.1819195 Fully adjusted      19140
##                   Est_CI          Category
## 1    10.36 (9.85, 10.88) Protective factor
## 2      9.51 (9.10, 9.93) Protective factor
## 3      7.95 (7.58, 8.32) Protective factor
## 4      7.25 (6.88, 7.61) Protective factor
## 5      5.53 (5.16, 5.89) Protective factor
## 6    10.39 (9.98, 10.79) Protective factor
## 7      9.17 (8.78, 9.57) Protective factor
## 8      7.81 (7.45, 8.18) Protective factor
## 9      7.04 (6.68, 7.41) Protective factor
## 10     5.53 (5.17, 5.89) Protective factor
## 11     6.49 (6.22, 6.77)       Risk factor
## 12    9.99 (9.65, 10.32)       Risk factor
## 13  11.87 (11.08, 12.65)       Risk factor
## 14  12.32 (11.43, 13.20)       Risk factor
## 15  12.72 (11.77, 13.68)       Risk factor
## 16     7.13 (6.83, 7.42)       Risk factor
## 17  10.80 (10.23, 11.36)       Risk factor
## 18  12.16 (10.33, 13.99)       Risk factor
## 19   10.73 (8.95, 12.52)       Risk factor
## 20  11.37 (10.05, 12.70)       Risk factor
## 21     6.95 (6.61, 7.28)       Risk factor
## 22  10.65 (10.20, 11.11)       Risk factor
## 23   11.00 (9.86, 12.14)       Risk factor
## 24   10.84 (9.76, 11.93)       Risk factor
## 25  11.56 (10.50, 12.62)       Risk factor
## 26     6.23 (5.92, 6.54)       Risk factor
## 27    9.98 (9.63, 10.33)       Risk factor
## 28  12.17 (11.54, 12.80)       Risk factor
## 29  13.13 (12.48, 13.78)       Risk factor
## 30  14.12 (13.40, 14.85)       Risk factor
## 31  10.63 (10.16, 11.10) Protective factor
## 32    9.91 (9.51, 10.31) Protective factor
## 33     9.15 (8.78, 9.51) Protective factor
## 34     7.11 (6.77, 7.45) Protective factor
## 35     5.39 (5.05, 5.74) Protective factor
## 36  12.38 (11.81, 12.96) Protective factor
## 37   10.02 (9.59, 10.46) Protective factor
## 38     9.51 (9.13, 9.89) Protective factor
## 39     7.50 (7.16, 7.84) Protective factor
## 40     6.02 (5.69, 6.36) Protective factor
## 41     8.09 (7.64, 8.55) Protective factor
## 42     7.50 (7.13, 7.86) Protective factor
## 43     6.22 (5.90, 6.54) Protective factor
## 44     5.79 (5.47, 6.10) Protective factor
## 45     4.48 (4.16, 4.80) Protective factor
## 46     8.13 (7.78, 8.49) Protective factor
## 47     7.24 (6.89, 7.58) Protective factor
## 48     6.15 (5.83, 6.46) Protective factor
## 49     5.59 (5.27, 5.90) Protective factor
## 50     4.50 (4.19, 4.81) Protective factor
## 51     5.25 (5.01, 5.49)       Risk factor
## 52     8.02 (7.73, 8.32)       Risk factor
## 53    9.53 (8.82, 10.23)       Risk factor
## 54    9.64 (8.84, 10.44)       Risk factor
## 55   10.15 (9.29, 11.01)       Risk factor
## 56     5.75 (5.49, 6.00)       Risk factor
## 57     8.63 (8.13, 9.12)       Risk factor
## 58    9.85 (8.19, 11.52)       Risk factor
## 59    8.59 (6.99, 10.19)       Risk factor
## 60    9.07 (7.87, 10.26)       Risk factor
## 61     5.52 (5.23, 5.81)       Risk factor
## 62     8.47 (8.07, 8.87)       Risk factor
## 63     8.68 (7.66, 9.69)       Risk factor
## 64     8.40 (7.42, 9.37)       Risk factor
## 65     8.87 (7.91, 9.82)       Risk factor
## 66     4.94 (4.67, 5.21)       Risk factor
## 67     7.92 (7.61, 8.23)       Risk factor
## 68    9.90 (9.33, 10.46)       Risk factor
## 69   10.36 (9.77, 10.95)       Risk factor
## 70  11.39 (10.73, 12.04)       Risk factor
## 71     8.02 (7.61, 8.44) Protective factor
## 72     7.65 (7.29, 8.00) Protective factor
## 73     7.09 (6.76, 7.41) Protective factor
## 74     5.63 (5.33, 5.93) Protective factor
## 75     4.42 (4.11, 4.72) Protective factor
## 76     9.24 (8.73, 9.76) Protective factor
## 77     7.73 (7.34, 8.12) Protective factor
## 78     7.38 (7.04, 7.72) Protective factor
## 79     5.93 (5.63, 6.22) Protective factor
## 80     4.82 (4.52, 5.11) Protective factor
## 81     0.43 (0.39, 0.46) Protective factor
## 82     0.29 (0.26, 0.32) Protective factor
## 83     0.22 (0.20, 0.25) Protective factor
## 84     0.19 (0.17, 0.22) Protective factor
## 85     0.15 (0.12, 0.17) Protective factor
## 86     0.33 (0.30, 0.36) Protective factor
## 87     0.27 (0.24, 0.30) Protective factor
## 88     0.21 (0.19, 0.23) Protective factor
## 89     0.18 (0.16, 0.21) Protective factor
## 90     0.16 (0.14, 0.18) Protective factor
## 91     0.13 (0.12, 0.15)       Risk factor
## 92     0.28 (0.26, 0.30)       Risk factor
## 93     0.39 (0.33, 0.46)       Risk factor
## 94     0.60 (0.53, 0.67)       Risk factor
## 95     0.66 (0.58, 0.74)       Risk factor
## 96     0.16 (0.14, 0.18)       Risk factor
## 97     0.36 (0.32, 0.41)       Risk factor
## 98     0.55 (0.41, 0.70)       Risk factor
## 99     0.73 (0.58, 0.87)       Risk factor
## 100    0.95 (0.84, 1.06)       Risk factor
## 101    0.18 (0.16, 0.20)       Risk factor
## 102    0.38 (0.35, 0.41)       Risk factor
## 103    0.45 (0.36, 0.54)       Risk factor
## 104    0.49 (0.41, 0.58)       Risk factor
## 105    0.77 (0.69, 0.86)       Risk factor
## 106    0.16 (0.14, 0.18)       Risk factor
## 107    0.29 (0.26, 0.31)       Risk factor
## 108    0.48 (0.42, 0.53)       Risk factor
## 109    0.49 (0.44, 0.54)       Risk factor
## 110    0.68 (0.62, 0.74)       Risk factor
## 111    0.39 (0.36, 0.43) Protective factor
## 112    0.29 (0.26, 0.32) Protective factor
## 113    0.26 (0.24, 0.29) Protective factor
## 114    0.19 (0.16, 0.21) Protective factor
## 115    0.15 (0.13, 0.18) Protective factor
## 116    0.52 (0.47, 0.56) Protective factor
## 117    0.32 (0.29, 0.36) Protective factor
## 118    0.29 (0.26, 0.32) Protective factor
## 119    0.20 (0.18, 0.23) Protective factor
## 120    0.16 (0.14, 0.18) Protective factor

saveRDS(dose_all, "out/dataframes/dose_all.rds")
writexl::write_xlsx(dose_all, "out/dataframes/dose_all.xlsx")

dose_resp_df <- 
  readRDS("out/dataframes/dose_all.rds") 

# Custom palette: Purple -> Teal -> Green -> Lime
viridis_lime_palette <- c(
  "#440154", # Dark Purple
  "lightblue", # Blue/Teal
  "#35B779", # Medium Green
  "#B4DE2C"  # Lime Green
)

# Assign these colors to labels
custom_colors <- c(
  # Risk Factors 
  "Bullying"                = viridis_lime_palette[1],
  "Sexual Harassment"       = viridis_lime_palette[2],
  "Violent threats"         = viridis_lime_palette[3],
  "Physical violence"       = viridis_lime_palette[4],
  
  # Protective Factors
  "Colleague support"       = viridis_lime_palette[1],
  "Support from superiors"  = viridis_lime_palette[2],
  "Meaning in work"         = viridis_lime_palette[3],
  "Purpose in work"         = viridis_lime_palette[4]
)


dose_plots <-
  
  dose_resp_df |> 
  group_by(Outcome, Exposure) |> 
  mutate(Level_Index = row_number()) |> 
  ungroup() |> 
  mutate(Outcome = factor(Outcome, 
                          levels = c("PHQ-9 score",
                                     "GAD-7 score",
                                     "PHQ-9 suicide item score")),
         Exposure = factor(Exposure, levels = exposure_order)) |> 
  arrange(Outcome) |> 
  ggplot(aes(x = Level_Index, 
             y = estimate, 
             group = Exposure,
             colour = Exposure)) +
  geom_line(linewidth = 0.5,
            position = position_dodge(width = 0.2)) + 
  geom_point(size = 2,
             position = position_dodge(width = 0.2)) + 
  facet_grid(rows = vars(Outcome),
             cols = vars(Category),
             scales = "free",
             switch = "y") + 
  scale_colour_manual(values = custom_colors) +
  scale_x_continuous(breaks = 1:5, minor_breaks = NULL) +
  labs(
    x = "Level of exposure",
    y = "Score change estimate",
    colour ="Exposure"
  ) + 
  facetted_pos_scales(
    y = list(
      Outcome == "PHQ-9 score" ~ scale_y_continuous(
        limits = c(0, 27),
        breaks = c(0, 5, 10, 15, 20, 25)),
      Outcome == "GAD-7 score" ~ scale_y_continuous(
        limits = c(0, 21),
        breaks = c(0, 5, 10, 15, 20)),
      Outcome == 
        "PHQ-9 suicide item score" ~ scale_y_continuous(
        limits = c(0, 4),
        breaks = c(0, 1, 2, 3, 4))
    )
  ) + 
  theme_minimal() +
  theme(
    strip.placement = "outside",  
    strip.text.y.left = element_text(angle = 90, face = "bold", size = 10), 
    strip.text.x = element_text(face = "bold", size = 10),
    axis.text.x = element_text(size = 10),
    axis.title.x = element_text(size = 10),
    axis.text.y = element_text(size = 10),
    panel.border = element_rect(color = "black", fill = NA),  
    axis.title.y = element_blank(), 
    legend.position = "right",
    legend.key.width = unit(1.5, "cm")
  )

dose_plots

ggsave(filename = "dose_resp_plots.pdf",
       plot = dose_plots,
       path = "out/plots/",
       width = 170,
       height = 170,
       units = "mm",
       dpi = 320,
       scale = 1.5
       )

ls() |> 
  str_subset("^glm_") |> 
  rm(list = _)

Survey approach

Define design

ds_ua <- 
  ds_ua |> 
  filter(!is.na(scdm_2_rec))

# As we do not know the absolute numbers but only distributions, we use
# raking for adjusting our sample to the population
# First, define population totals (marginal targets)
pop_profession <- 
  data.frame(
    work_2 = c("Doctor", "Nurse"),
    Freq = c(155725, 243664)
    )

# Calculate margins for gender but with absolute totals
# Female: (155,725 * 0.65) + (243,664 * 0.98) = 339,912
# Male: (155,725 * 0.35) + (243,664 * 0.02) = 59,477

pop_gender <- 
  data.frame(
    scdm_2_rec = c("Female", "Male"),
    Freq = c(339912, 59477)
    )



svy_ua <- 
  svydesign(
    data = ds_ua,
    ids = ~loc_3,  # participants may be more similar within regions
    weights = NULL
    )

# raking is a good practice when applying convenience sampling
svy_ua_w <- rake(
  design = svy_ua,
  sample.margins = list(~work_2, ~scdm_2_rec),
  population.margins = list(pop_profession, pop_gender)
)



rm(pop_profession, pop_gender)

Tables

Sociodemographic

# tbl_svysummary has issues with stratifying, we can only gen information
# on separate columns

# define vars
scdm_vars <- c("scdm_1_rec", "scdm_4_dic", "scdm_5_dic", "work_1", 
               "work_3_rec", "work_5_rec", "conflict")


# We do this first so we can use them in the labels later
n_counts <- 
  ds_ua |> 
  count(work_2, scdm_2_rec)

n_total_counts <- 
  ds_ua |> 
  count(work_2) # For the Overall headers

get_n_style <- 
  function(w, g = NULL) {
    
    if(is.null(g)) {
      
      val <- 
        n_total_counts |> 
        filter(work_2 == w) |> 
        pull(n)
      
      } else {
        
        val <- 
          n_counts |> 
          filter(work_2 == w, 
                 scdm_2_rec == g) |> 
          pull(n)
  }
  style_number(val)
}

# Doctors
n_doc_all <- get_n_style("Doctor")
n_doc_m   <- get_n_style("Doctor", "Male")
n_doc_f   <- get_n_style("Doctor", "Female")

# Nurses
n_nur_all <- get_n_style("Nurse")
n_nur_m   <- get_n_style("Nurse", "Male")
n_nur_f   <- get_n_style("Nurse", "Female")


# Summaries (With add_overall) 

doc_n <- 
  ds_ua |> 
  filter(work_2 == "Doctor") |>
  tbl_summary(
    by = scdm_2_rec,
    include = all_of(scdm_vars),
    statistic = list(all_categorical() ~ "{n}"),
    missing = "no"
  ) |>
  add_overall() |> # Adds stat_0
  modify_header(all_stat_cols() ~ "**{level}** (n)") 

doc_p <- 
  subset(svy_ua_w, work_2 == "Doctor") |>
  tbl_svysummary(
    by = scdm_2_rec,
    include = all_of(scdm_vars),
    statistic = list(all_categorical() ~ "({p}%)"),
    missing = "no"
  ) |>
  add_overall() |> # Adds stat_0
  modify_header(all_stat_cols() ~ "**{level}** (%)")

doc_merged <- 
  tbl_merge(list(doc_n, doc_p), 
            tab_spanner = FALSE)


nur_n <- 
  ds_ua |> 
  filter(work_2 == "Nurse") |>
  tbl_summary(
    by = scdm_2_rec,
    include = all_of(scdm_vars),
    statistic = list(all_categorical() ~ "{n}"),
    missing = "no"
  ) |>
  add_overall() |>
  modify_header(all_stat_cols() ~ "**{level}** (n)")

nur_p <- 
  subset(svy_ua_w, work_2 == "Nurse") |>
  tbl_svysummary(
    by = scdm_2_rec,
    include = all_of(scdm_vars),
    statistic = list(all_categorical() ~ "({p}%)"),
    missing = "no"
  ) |>
  add_overall() |>
  modify_header(all_stat_cols() ~ "**{level}** (%)")

nur_merged <- 
  tbl_merge(list(nur_n, nur_p), 
            tab_spanner = FALSE)


# Merge and GT

scdm_tbl_w <- 
  
  tbl_merge(
    list(doc_merged, nur_merged),
    tab_spanner = c("**Doctor**", "**Nurse**")
  ) |>  
  modify_footnote(everything() ~ NA) |> 
  as_gt() 

# Column merging

# LOGIC:
# stat_0 = Overall
# stat_1 = Female (Alphabetical F < M)
# stat_2 = Male
#
# Suffixes added by tbl_merge:
# _1 = Counts (from the first merge n+p)
# _2 = Props  (from the first merge n+p)
# _1 (outer) = Doctors
# _2 (outer) = Nurses

scdm_tbl_w <- 
  scdm_tbl_w |>

  cols_merge(columns = c("stat_0_1_1", "stat_0_2_1")) |> 
  cols_merge(columns = c("stat_1_1_1", "stat_1_2_1")) |> 
  cols_merge(columns = c("stat_2_1_1", "stat_2_2_1")) |> 
  cols_merge(columns = c("stat_0_1_2", "stat_0_2_2")) |> 
  cols_merge(columns = c("stat_1_1_2", "stat_1_2_2")) |> 
  cols_merge(columns = c("stat_2_1_2", "stat_2_2_2")) |> 
  cols_label(
    stat_0_1_1 = md(glue::glue("**Overall**<br>N = {n_doc_all}")), 
    stat_1_1_1 = md(glue::glue("**Female**<br>N = {n_doc_f}")), 
    stat_2_1_1 = md(glue::glue("**Male**<br>N = {n_doc_m}")),
    stat_0_1_2 = md(glue::glue("**Overall**<br>N = {n_nur_all}")),
    stat_1_1_2 = md(glue::glue("**Female**<br>N = {n_nur_f}")),
    stat_2_1_2 = md(glue::glue("**Male**<br>N = {n_nur_m}"))
  ) |>
  tab_footnote(
    footnote = "n (weighted %)",
    locations = cells_column_labels(
      columns = c(stat_0_1_1, stat_1_1_1, stat_2_1_1, 
                  stat_0_1_2, stat_1_1_2, stat_2_1_2)
    )
  )

# View
scdm_tbl_w

Characteristic	Doctor			Nurse
	Overall N = 10,1581	Female N = 7,6621	Male N = 2,4961	Overall N = 13,5791	Female N = 12,7441	Male N = 8351
Age groups (Recoded)
20-30	1,694 (17%)	1,214 (16%)	480 (19%)	2,142 (16%)	1,866 (15%)	276 (33%)
31-45	3,330 (33%)	2,567 (34%)	763 (31%)	5,181 (38%)	4,931 (39%)	250 (30%)
46-55	2,063 (20%)	1,561 (20%)	502 (20%)	3,999 (29%)	3,793 (30%)	206 (25%)
Over 55	3,071 (30%)	2,320 (30%)	751 (30%)	2,257 (17%)	2,154 (17%)	103 (12%)
Relationship status
No partner	3,507 (34%)	2,843 (37%)	664 (27%)	4,541 (33%)	4,233 (33%)	308 (37%)
Has a partner	6,639 (66%)	4,811 (63%)	1,828 (73%)	9,027 (67%)	8,500 (67%)	527 (63%)
Having children in the household
Has children	6,449 (63%)	4,940 (65%)	1,509 (61%)	9,982 (73%)	9,497 (75%)	485 (58%)
No children	3,692 (37%)	2,711 (35%)	981 (39%)	3,574 (27%)	3,224 (25%)	350 (42%)
Post-graduate training	1,026 (10%)	738 (9.7%)	288 (12%)	1,140 (8.5%)	1,026 (8.1%)	114 (14%)
Length of service (Recoded)
5 years or less	1,836 (18%)	1,371 (18%)	465 (19%)	1,281 (9.7%)	1,087 (8.7%)	194 (24%)
6-10 years	1,209 (12%)	885 (12%)	324 (13%)	1,431 (11%)	1,310 (10%)	121 (15%)
More than 10 years	6,943 (69%)	5,272 (70%)	1,671 (68%)	10,613 (79%)	10,117 (81%)	496 (61%)
Healthcare setting (Recoded)
Outpatient care	6,000 (60%)	4,935 (66%)	1,065 (43%)	5,703 (43%)	5,571 (45%)	132 (16%)
In-hospital care	3,117 (32%)	2,054 (27%)	1,063 (43%)	5,030 (38%)	4,920 (40%)	110 (14%)
Emergencies	835 (8.6%)	508 (6.8%)	327 (13%)	2,417 (19%)	1,856 (15%)	561 (70%)
Proximity to combat areas and occupied territories
No conflict zone	8,440 (83%)	6,304 (82%)	2,136 (86%)	11,031 (81%)	10,345 (81%)	686 (82%)
Conflict zone	1,718 (17%)	1,358 (18%)	360 (14%)	2,548 (19%)	2,399 (19%)	149 (18%)
1 n (weighted %)

# Save
gtsave(scdm_tbl_w, "out/tables/scdm_tbl_w.docx")

rm(n_counts, n_m_doc, n_m_nur, n_f_doc, n_f_nur, nur_n, nur_p,
   doc_merged, doc_n, doc_p, nur_merged)

# Run functions 
tbl_depression_w <- 
  generate_outcome_table_w_out("phq_co", "Probable Depression")
gtsave(tbl_depression_w, "out/tables/dep_by_scdm_w.docx")

tbl_anxiety_w <- 
  generate_outcome_table_w_out("gad_co", "Probable Anxiety")
gtsave(tbl_anxiety_w, "out/tables/anx_by_scdm_w.docx")

tbl_suicide_w <- 
  generate_outcome_table_w_out("suic_idea", "Passive Suicide Thoughts")
gtsave(tbl_suicide_w, "out/tables/suic_by_scdm_w.docx")


tbl_depression_w

Probable Depression
	Doctor			Nurse
	Male (N = 2,129)	Female (N = 6,431)	Overall (N = 8,560)	Male (N = 677)	Female (N = 10,313)	Overall (N = 10,990)
Overall
Overall	33.8% (30.9-36.8)	35.5% (33.4-37.7)	35.1% (33.1-37.1)	27.5% (22.8-32.5)	22.4% (20.1-24.9)	22.8% (20.4-25.3)
Age groups (Recoded)
20-30	46.1% (40.6-51.6)	48.3% (45.1-51.5)	47.6% (44.6-50.6)	34.8% (27.2-43.1)	32.7% (27.7-37.9)	33.0% (28.1-38.1)
31-45	45.8% (41.5-50.1)	42.3% (39.2-45.5)	43.2% (40.3-46.2)	29.9% (24.7-35.5)	23.8% (20.4-27.5)	24.2% (20.7-27.9)
46-55	27.4% (23.2-31.9)	36.2% (33.6-38.8)	33.8% (31.6-36.1)	23.7% (19.4-28.3)	22.3% (20.2-24.5)	22.4% (20.3-24.6)
Over 55	17.8% (15.1-20.8)	21.0% (18.7-23.5)	20.2% (18.0-22.4)	8.8% (3.7-17.0)	10.7% (9.0-12.7)	10.6% (8.8-12.7)
Relationship status
No partner	44.8% (40.0-49.7)	36.2% (33.6-38.9)	38.0% (35.6-40.5)	31.5% (23.9-39.9)	25.1% (23.2-27.1)	25.7% (23.7-27.7)
Has a partner	30.0% (26.6-33.4)	35.1% (33.1-37.2)	33.5% (31.5-35.6)	25.1% (20.3-30.3)	21.1% (18.4-24.0)	21.4% (18.6-24.3)
Having children in the household
Has children	29.8% (26.7-33.1)	33.6% (31.4-35.9)	32.6% (30.5-34.8)	25.6% (20.3-31.6)	21.3% (18.6-24.1)	21.5% (18.8-24.4)
No children	39.8% (35.4-44.4)	39.0% (36.4-41.7)	39.3% (36.8-41.7)	30.0% (22.4-38.6)	25.9% (24.0-27.9)	26.4% (24.4-28.5)
Post-graduate training
No	35.0% (31.9-38.1)	35.8% (33.8-37.9)	35.6% (33.6-37.6)	27.0% (21.7-32.8)	22.6% (20.0-25.3)	22.9% (20.2-25.6)
Yes	24.3% (19.5-29.7)	32.5% (25.6-40.0)	29.8% (24.5-35.6)	31.0% (17.4-47.6)	20.7% (17.4-24.3)	21.9% (18.4-25.7)
Length of service (Recoded)
5 years or less	42.3% (36.6-48.2)	45.2% (42.1-48.2)	44.3% (41.6-47.1)	34.0% (25.9-42.8)	32.3% (25.8-39.2)	32.6% (26.8-38.7)
6-10 years	51.1% (44.5-57.5)	47.7% (43.9-51.5)	48.7% (45.4-52.0)	32.3% (21.7-44.5)	27.0% (23.5-30.7)	27.5% (24.3-31.0)
More than 10 years	27.9% (25.0-31.0)	31.1% (28.8-33.4)	30.3% (28.1-32.5)	24.1% (21.1-27.3)	20.9% (18.6-23.3)	21.1% (18.8-23.5)
Healthcare setting (Recoded)
Outpatient care	32.9% (28.2-37.9)	34.9% (32.5-37.3)	34.5% (32.0-37.0)	25.9% (18.8-34.0)	22.8% (20.2-25.5)	22.9% (20.2-25.6)
In-hospital care	34.8% (31.6-38.1)	36.1% (33.9-38.3)	35.6% (33.7-37.6)	25.8% (16.9-36.5)	20.9% (18.2-23.8)	21.0% (18.3-24.0)
Emergencies	33.7% (22.1-46.9)	40.5% (31.4-50.1)	37.5% (27.7-48.2)	28.2% (23.4-33.4)	25.9% (22.3-29.7)	26.5% (23.0-30.2)
Proximity to combat areas and occupied territories
No conflict zone	33.8% (30.5-37.2)	35.5% (33.3-37.7)	35.0% (32.8-37.3)	26.3% (20.8-32.4)	21.9% (19.1-24.9)	22.2% (19.4-25.2)
Conflict zone	34.0% (26.6-42.0)	35.9% (28.6-43.8)	35.5% (29.5-41.8)	32.5% (27.9-37.4)	24.9% (21.6-28.4)	25.4% (22.5-28.5)
Weighted Prevalence (95% CI)

tbl_anxiety_w

Probable Anxiety
	Doctor			Nurse
	Male (N = 2,134)	Female (N = 6,438)	Overall (N = 8,572)	Male (N = 680)	Female (N = 10,323)	Overall (N = 11,003)
Overall
Overall	21.7% (19.6-23.9)	25.7% (24.0-27.4)	24.6% (23.1-26.1)	16.3% (12.0-21.5)	16.6% (14.7-18.6)	16.6% (14.7-18.5)
Age groups (Recoded)
20-30	29.3% (24.7-34.3)	36.5% (33.3-39.8)	34.2% (31.5-37.1)	20.1% (12.2-30.1)	24.2% (20.1-28.7)	23.6% (19.6-27.9)
31-45	29.2% (25.8-32.9)	30.9% (28.5-33.3)	30.4% (28.4-32.6)	18.0% (11.4-26.5)	16.6% (14.1-19.3)	16.6% (14.2-19.4)
46-55	17.7% (15.0-20.7)	25.7% (23.4-28.1)	23.5% (21.7-25.4)	13.0% (7.6-20.3)	16.9% (15.5-18.4)	16.7% (15.3-18.2)
Over 55	11.6% (9.0-14.7)	14.4% (12.7-16.2)	13.6% (11.9-15.5)	8.5% (2.8-18.9)	9.5% (7.9-11.2)	9.4% (7.9-11.1)
Relationship status
No partner	28.9% (24.5-33.5)	25.5% (23.4-27.7)	26.2% (24.3-28.3)	19.6% (13.1-27.7)	18.4% (16.8-20.2)	18.5% (16.7-20.4)
Has a partner	19.2% (16.9-21.7)	25.8% (23.9-27.7)	23.8% (22.2-25.4)	14.4% (10.4-19.1)	15.7% (13.4-18.2)	15.6% (13.4-18.0)
Having children in the household
Has children	19.3% (17.3-21.4)	23.9% (22.2-25.7)	22.7% (21.1-24.4)	14.1% (10.5-18.4)	15.8% (13.6-18.1)	15.7% (13.6-17.9)
No children	25.3% (21.5-29.5)	28.9% (26.9-30.9)	27.8% (26.2-29.6)	19.4% (11.9-29.1)	19.0% (17.4-20.7)	19.1% (17.3-21.0)
Post-graduate training
No	22.1% (19.9-24.4)	26.0% (24.2-27.8)	24.9% (23.4-26.5)	16.6% (12.2-21.8)	16.7% (14.6-18.9)	16.7% (14.7-18.9)
Yes	18.2% (14.0-23.1)	22.4% (18.5-26.7)	21.0% (18.0-24.3)	14.8% (4.5-32.7)	15.1% (12.5-18.0)	15.0% (11.9-18.6)
Length of service (Recoded)
5 years or less	26.4% (21.0-32.4)	33.8% (30.6-37.0)	31.7% (28.7-34.7)	19.0% (9.8-31.4)	24.5% (20.1-29.3)	23.5% (19.9-27.4)
6-10 years	32.0% (27.0-37.4)	35.4% (32.4-38.4)	34.4% (31.9-36.9)	19.0% (9.3-32.6)	20.1% (16.5-24.1)	20.0% (16.3-24.2)
More than 10 years	18.4% (16.3-20.7)	22.1% (20.2-24.0)	21.1% (19.4-22.8)	14.8% (12.0-18.0)	15.4% (13.7-17.2)	15.3% (13.7-17.1)
Healthcare setting (Recoded)
Outpatient care	22.1% (18.5-26.0)	25.9% (24.0-27.9)	25.2% (23.2-27.2)	16.1% (9.5-24.7)	17.4% (15.6-19.5)	17.4% (15.5-19.4)
In-hospital care	22.2% (19.3-25.2)	24.8% (22.8-26.8)	23.8% (22.3-25.3)	15.7% (7.8-27.0)	14.6% (12.5-17.0)	14.7% (12.5-17.0)
Emergencies	19.2% (13.0-26.7)	27.2% (21.9-33.1)	23.7% (18.3-29.8)	16.5% (11.2-23.1)	19.5% (16.9-22.3)	18.7% (15.7-22.0)
Proximity to combat areas and occupied territories
No conflict zone	21.8% (19.5-24.3)	25.4% (23.6-27.2)	24.4% (22.7-26.1)	15.5% (10.5-21.8)	15.9% (13.8-18.1)	15.9% (13.8-18.1)
Conflict zone	20.8% (15.5-27.0)	27.2% (22.4-32.4)	25.7% (22.4-29.3)	19.8% (12.8-28.6)	19.6% (16.7-22.7)	19.6% (16.6-22.9)
Weighted Prevalence (95% CI)

tbl_suicide_w

Passive Suicide Thoughts
	Doctor			Nurse
	Male (N = 2,137)	Female (N = 6,460)	Overall (N = 8,597)	Male (N = 682)	Female (N = 10,367)	Overall (N = 11,049)
Overall
Overall	15.2% (13.3-17.2)	11.4% (10.5-12.3)	12.4% (11.5-13.4)	12.6% (9.1-16.9)	8.0% (6.8-9.3)	8.3% (7.1-9.7)
Age groups (Recoded)
20-30	22.5% (18.3-27.2)	18.4% (16.2-20.6)	19.7% (17.6-21.9)	17.3% (11.1-25.3)	14.1% (11.4-17.1)	14.5% (11.8-17.7)
31-45	20.8% (16.6-25.4)	13.1% (11.4-15.1)	15.1% (13.2-17.3)	11.7% (7.1-17.8)	7.7% (6.3-9.2)	7.9% (6.5-9.4)
46-55	11.4% (8.8-14.5)	11.1% (9.8-12.5)	11.2% (10.0-12.6)	10.7% (5.7-17.6)	7.8% (6.6-9.3)	8.0% (6.7-9.4)
Over 55	7.3% (5.6-9.4)	6.0% (5.2-6.9)	6.4% (5.7-7.1)	6.0% (2.6-11.6)	3.8% (3.0-4.8)	3.9% (3.1-4.9)
Relationship status
No partner	21.7% (16.9-27.1)	13.5% (12.0-15.0)	15.2% (13.5-17.0)	16.0% (10.5-23.0)	10.5% (9.1-12.0)	10.9% (9.3-12.6)
Has a partner	13.0% (11.1-15.0)	10.2% (9.4-11.0)	11.0% (10.1-12.0)	10.6% (7.3-14.6)	6.8% (5.6-8.1)	7.0% (5.8-8.4)
Having children in the household
Has children	12.8% (11.5-14.2)	10.1% (9.4-10.9)	10.8% (10.2-11.5)	10.3% (6.9-14.7)	7.0% (5.8-8.4)	7.2% (6.0-8.5)
No children	18.9% (15.1-23.1)	13.6% (12.0-15.4)	15.2% (13.4-17.1)	15.8% (9.9-23.4)	10.8% (9.2-12.6)	11.4% (9.5-13.5)
Post-graduate training
No	15.1% (13.3-17.1)	11.2% (10.4-12.1)	12.3% (11.4-13.2)	12.3% (8.7-16.8)	7.9% (6.7-9.2)	8.2% (7.0-9.5)
Yes	15.9% (11.9-20.7)	12.8% (9.8-16.3)	13.8% (11.2-16.8)	14.8% (8.3-23.5)	9.4% (6.7-12.7)	10.1% (7.4-13.3)
Length of service (Recoded)
5 years or less	21.9% (17.3-27.0)	16.0% (13.8-18.4)	17.6% (15.5-19.9)	20.8% (13.4-29.9)	13.0% (9.7-16.8)	14.3% (11.0-18.2)
6-10 years	24.6% (18.9-31.0)	15.4% (13.1-18.1)	18.2% (15.9-20.6)	13.0% (6.1-23.2)	12.8% (10.2-15.9)	12.9% (10.3-15.8)
More than 10 years	11.6% (10.2-13.2)	9.6% (8.8-10.3)	10.1% (9.4-10.9)	9.6% (6.3-14.0)	6.9% (5.9-8.0)	7.0% (6.0-8.2)
Healthcare setting (Recoded)
Outpatient care	15.6% (12.6-18.9)	11.0% (10.2-12.0)	11.9% (10.8-13.1)	9.7% (4.7-17.3)	7.9% (6.7-9.2)	7.9% (6.7-9.3)
In-hospital care	15.6% (13.9-17.4)	11.1% (9.7-12.7)	12.8% (11.6-14.0)	10.1% (4.6-18.7)	7.2% (6.0-8.6)	7.3% (6.0-8.7)
Emergencies	13.0% (7.9-19.8)	15.7% (9.9-23.2)	14.5% (9.3-21.3)	13.8% (9.9-18.5)	10.5% (8.3-13.0)	11.4% (9.2-13.8)
Proximity to combat areas and occupied territories
No conflict zone	15.8% (13.6-18.2)	11.2% (10.2-12.3)	12.5% (11.4-13.7)	12.2% (7.9-17.9)	7.8% (6.4-9.3)	8.1% (6.6-9.8)
Conflict zone	11.6% (9.4-14.1)	12.1% (10.7-13.6)	12.0% (10.7-13.3)	14.3% (10.2-19.3)	8.8% (6.8-11.2)	9.2% (7.2-11.6)
Weighted Prevalence (95% CI)

# Generate tables with function
tbl_harassment_w <- generate_exposure_table_w_exp("work_30_dic", "Sexual harassment")
gtsave(tbl_harassment_w, "out/tables/harass_by_scdm_w.docx")

tbl_threats_w <- generate_exposure_table_w_exp("work_31_dic", "Violent threats")
gtsave(tbl_threats_w, "out/tables/threats_by_scdm_w.docx")

tbl_violence_w <- generate_exposure_table_w_exp("work_32_dic", "Physical violence")
gtsave(tbl_violence_w, "out/tables/violence_by_scdm_w.docx")

tbl_bullying_w <- generate_exposure_table_w_exp("work_33_dic", "Bullying")
gtsave(tbl_bullying_w, "out/tables/bullying_by_scdm_w.docx")

tbl_colleagues_w <- generate_exposure_table_w_exp("work_21_dic", "Colleague support")
gtsave(tbl_colleagues_w, "out/tables/col_supp_by_scdm_w.docx")

tbl_superiors_w <- generate_exposure_table_w_exp("work_22_dic", 
                                         "Support from superiors")
gtsave(tbl_superiors_w, "out/tables/sup_supp_by_scdm_w.docx")

tbl_meaning_w <- generate_exposure_table_w_exp("wb_wami_1_dic", "Meaning in work")
gtsave(tbl_meaning_w, "out/tables/meaning_by_scdm_w.docx")

tbl_purpose_w <- generate_exposure_table_w_exp("wb_wami_2_dic", "Purpose in work")
gtsave(tbl_purpose_w, "out/tables/purpose_by_scdm_w.docx")



# Visualize

tbl_harassment_w

Sexual harassment
	Doctor			Nurse
	Male (N = 2,226)	Female (N = 6,705)	Overall (N = 8,931)	Male (N = 716)	Female (N = 10,765)	Overall (N = 11,481)
Overall
Overall	8.2% (7.2-9.3)	9.8% (9.0-10.5)	9.3% (8.7-10.0)	8.8% (7.1-10.7)	6.8% (6.0-7.6)	6.9% (6.1-7.7)
Age groups (Recoded)
20-30	54.9% (48.6-61.2)	60.0% (57.1-62.9)	58.4% (55.8-61.0)	57.0% (50.3-63.5)	58.7% (55.8-61.6)	58.4% (55.6-61.2)
31-45	53.0% (48.9-57.1)	54.8% (52.7-57.0)	54.4% (52.7-56.0)	48.8% (41.1-56.6)	54.5% (52.9-56.1)	54.2% (52.5-55.8)
46-55	60.7% (57.3-64.1)	60.3% (57.2-63.4)	60.4% (58.0-62.9)	56.5% (47.6-65.1)	59.5% (57.9-61.2)	59.4% (58.0-60.7)
Over 55	65.8% (62.1-69.4)	65.0% (62.3-67.5)	65.2% (62.9-67.4)	58.2% (46.4-69.4)	64.4% (62.1-66.7)	64.1% (61.9-66.2)
Relationship status
No partner	54.1% (48.9-59.3)	60.0% (57.0-63.0)	58.8% (56.0-61.5)	54.9% (48.2-61.5)	58.0% (55.9-60.1)	57.7% (55.7-59.8)
Has a partner	60.4% (58.2-62.5)	59.7% (57.9-61.5)	59.9% (58.6-61.2)	54.4% (49.5-59.2)	58.4% (57.1-59.7)	58.1% (56.9-59.3)
Having children in the household
Has children	60.0% (57.7-62.3)	60.0% (57.9-62.0)	60.0% (58.5-61.5)	54.0% (48.7-59.2)	57.9% (56.5-59.3)	57.7% (56.4-59.0)
No children	56.7% (52.5-60.8)	59.5% (56.9-62.1)	58.7% (56.5-60.8)	55.4% (51.0-59.8)	59.3% (57.6-60.9)	58.8% (57.1-60.5)
Post-graduate training
No	59.2% (56.7-61.7)	60.0% (58.1-61.9)	59.8% (58.4-61.1)	54.0% (49.7-58.4)	58.7% (57.5-60.0)	58.4% (57.4-59.4)
Yes	55.0% (48.7-61.2)	58.4% (53.3-63.3)	57.2% (52.9-61.6)	57.7% (43.9-70.8)	52.2% (47.8-56.5)	52.8% (49.0-56.7)
Length of service (Recoded)
5 years or less	57.7% (52.6-62.8)	61.6% (58.4-64.7)	60.5% (58.0-62.9)	61.7% (51.7-71.0)	60.6% (56.6-64.4)	60.8% (56.6-64.9)
6-10 years	50.7% (44.1-57.2)	55.7% (50.3-61.0)	54.2% (49.2-59.1)	51.4% (35.6-66.9)	57.6% (54.2-61.0)	57.0% (53.7-60.3)
More than 10 years	60.5% (58.1-62.8)	60.1% (58.2-62.0)	60.2% (58.7-61.7)	53.0% (48.6-57.4)	58.1% (56.9-59.3)	57.8% (56.8-58.8)
Healthcare setting (Recoded)
Outpatient care	58.3% (54.8-61.7)	58.2% (56.0-60.4)	58.2% (56.5-59.9)	66.7% (56.3-76.0)	59.2% (57.2-61.1)	59.4% (57.5-61.2)
In-hospital care	59.6% (55.7-63.4)	63.1% (60.3-65.9)	61.8% (59.6-64.0)	57.0% (47.5-66.1)	57.8% (55.8-59.8)	57.8% (55.8-59.8)
Emergencies	57.6% (53.0-62.0)	62.2% (57.0-67.2)	60.2% (56.7-63.6)	51.4% (48.6-54.1)	56.9% (55.2-58.5)	55.4% (53.9-56.9)
Proximity to combat areas and occupied territories
No conflict zone	58.0% (55.4-60.5)	59.7% (57.5-61.9)	59.2% (57.6-60.8)	55.4% (50.8-60.0)	58.2% (56.9-59.6)	58.0% (56.9-59.2)
Conflict zone	63.2% (52.5-73.1)	60.5% (57.4-63.4)	61.1% (56.8-65.3)	50.8% (40.4-61.0)	58.3% (56.6-60.0)	57.8% (56.2-59.3)
Weighted Prevalence (95% CI)

tbl_threats_w

Violent threats
	Doctor			Nurse
	Male (N = 2,218)	Female (N = 6,689)	Overall (N = 8,907)	Male (N = 715)	Female (N = 10,744)	Overall (N = 11,459)
Overall
Overall	27.2% (24.0-30.6)	17.6% (15.6-19.8)	20.3% (18.3-22.4)	29.0% (23.1-35.4)	10.2% (8.3-12.5)	11.6% (9.2-14.4)
Age groups (Recoded)
20-30	54.9% (48.6-61.2)	60.0% (57.1-62.9)	58.4% (55.8-61.0)	57.0% (50.3-63.5)	58.7% (55.8-61.6)	58.4% (55.6-61.2)
31-45	53.0% (48.9-57.1)	54.8% (52.7-57.0)	54.4% (52.7-56.0)	48.8% (41.1-56.6)	54.5% (52.9-56.1)	54.2% (52.5-55.8)
46-55	60.7% (57.3-64.1)	60.3% (57.2-63.4)	60.4% (58.0-62.9)	56.5% (47.6-65.1)	59.5% (57.9-61.2)	59.4% (58.0-60.7)
Over 55	65.8% (62.1-69.4)	65.0% (62.3-67.5)	65.2% (62.9-67.4)	58.2% (46.4-69.4)	64.4% (62.1-66.7)	64.1% (61.9-66.2)
Relationship status
No partner	54.1% (48.9-59.3)	60.0% (57.0-63.0)	58.8% (56.0-61.5)	54.9% (48.2-61.5)	58.0% (55.9-60.1)	57.7% (55.7-59.8)
Has a partner	60.4% (58.2-62.5)	59.7% (57.9-61.5)	59.9% (58.6-61.2)	54.4% (49.5-59.2)	58.4% (57.1-59.7)	58.1% (56.9-59.3)
Having children in the household
Has children	60.0% (57.7-62.3)	60.0% (57.9-62.0)	60.0% (58.5-61.5)	54.0% (48.7-59.2)	57.9% (56.5-59.3)	57.7% (56.4-59.0)
No children	56.7% (52.5-60.8)	59.5% (56.9-62.1)	58.7% (56.5-60.8)	55.4% (51.0-59.8)	59.3% (57.6-60.9)	58.8% (57.1-60.5)
Post-graduate training
No	59.2% (56.7-61.7)	60.0% (58.1-61.9)	59.8% (58.4-61.1)	54.0% (49.7-58.4)	58.7% (57.5-60.0)	58.4% (57.4-59.4)
Yes	55.0% (48.7-61.2)	58.4% (53.3-63.3)	57.2% (52.9-61.6)	57.7% (43.9-70.8)	52.2% (47.8-56.5)	52.8% (49.0-56.7)
Length of service (Recoded)
5 years or less	57.7% (52.6-62.8)	61.6% (58.4-64.7)	60.5% (58.0-62.9)	61.7% (51.7-71.0)	60.6% (56.6-64.4)	60.8% (56.6-64.9)
6-10 years	50.7% (44.1-57.2)	55.7% (50.3-61.0)	54.2% (49.2-59.1)	51.4% (35.6-66.9)	57.6% (54.2-61.0)	57.0% (53.7-60.3)
More than 10 years	60.5% (58.1-62.8)	60.1% (58.2-62.0)	60.2% (58.7-61.7)	53.0% (48.6-57.4)	58.1% (56.9-59.3)	57.8% (56.8-58.8)
Healthcare setting (Recoded)
Outpatient care	58.3% (54.8-61.7)	58.2% (56.0-60.4)	58.2% (56.5-59.9)	66.7% (56.3-76.0)	59.2% (57.2-61.1)	59.4% (57.5-61.2)
In-hospital care	59.6% (55.7-63.4)	63.1% (60.3-65.9)	61.8% (59.6-64.0)	57.0% (47.5-66.1)	57.8% (55.8-59.8)	57.8% (55.8-59.8)
Emergencies	57.6% (53.0-62.0)	62.2% (57.0-67.2)	60.2% (56.7-63.6)	51.4% (48.6-54.1)	56.9% (55.2-58.5)	55.4% (53.9-56.9)
Proximity to combat areas and occupied territories
No conflict zone	58.0% (55.4-60.5)	59.7% (57.5-61.9)	59.2% (57.6-60.8)	55.4% (50.8-60.0)	58.2% (56.9-59.6)	58.0% (56.9-59.2)
Conflict zone	63.2% (52.5-73.1)	60.5% (57.4-63.4)	61.1% (56.8-65.3)	50.8% (40.4-61.0)	58.3% (56.6-60.0)	57.8% (56.2-59.3)
Weighted Prevalence (95% CI)

tbl_violence_w

Physical violence
	Doctor			Nurse
	Male (N = 2,218)	Female (N = 6,677)	Overall (N = 8,895)	Male (N = 713)	Female (N = 10,733)	Overall (N = 11,446)
Overall
Overall	5.5% (4.0-7.4)	2.5% (2.0-3.1)	3.3% (2.7-4.1)	10.9% (8.1-14.4)	3.1% (2.5-3.8)	3.7% (2.9-4.6)
Age groups (Recoded)
20-30	54.9% (48.6-61.2)	60.0% (57.1-62.9)	58.4% (55.8-61.0)	57.0% (50.3-63.5)	58.7% (55.8-61.6)	58.4% (55.6-61.2)
31-45	53.0% (48.9-57.1)	54.8% (52.7-57.0)	54.4% (52.7-56.0)	48.8% (41.1-56.6)	54.5% (52.9-56.1)	54.2% (52.5-55.8)
46-55	60.7% (57.3-64.1)	60.3% (57.2-63.4)	60.4% (58.0-62.9)	56.5% (47.6-65.1)	59.5% (57.9-61.2)	59.4% (58.0-60.7)
Over 55	65.8% (62.1-69.4)	65.0% (62.3-67.5)	65.2% (62.9-67.4)	58.2% (46.4-69.4)	64.4% (62.1-66.7)	64.1% (61.9-66.2)
Relationship status
No partner	54.1% (48.9-59.3)	60.0% (57.0-63.0)	58.8% (56.0-61.5)	54.9% (48.2-61.5)	58.0% (55.9-60.1)	57.7% (55.7-59.8)
Has a partner	60.4% (58.2-62.5)	59.7% (57.9-61.5)	59.9% (58.6-61.2)	54.4% (49.5-59.2)	58.4% (57.1-59.7)	58.1% (56.9-59.3)
Having children in the household
Has children	60.0% (57.7-62.3)	60.0% (57.9-62.0)	60.0% (58.5-61.5)	54.0% (48.7-59.2)	57.9% (56.5-59.3)	57.7% (56.4-59.0)
No children	56.7% (52.5-60.8)	59.5% (56.9-62.1)	58.7% (56.5-60.8)	55.4% (51.0-59.8)	59.3% (57.6-60.9)	58.8% (57.1-60.5)
Post-graduate training
No	59.2% (56.7-61.7)	60.0% (58.1-61.9)	59.8% (58.4-61.1)	54.0% (49.7-58.4)	58.7% (57.5-60.0)	58.4% (57.4-59.4)
Yes	55.0% (48.7-61.2)	58.4% (53.3-63.3)	57.2% (52.9-61.6)	57.7% (43.9-70.8)	52.2% (47.8-56.5)	52.8% (49.0-56.7)
Length of service (Recoded)
5 years or less	57.7% (52.6-62.8)	61.6% (58.4-64.7)	60.5% (58.0-62.9)	61.7% (51.7-71.0)	60.6% (56.6-64.4)	60.8% (56.6-64.9)
6-10 years	50.7% (44.1-57.2)	55.7% (50.3-61.0)	54.2% (49.2-59.1)	51.4% (35.6-66.9)	57.6% (54.2-61.0)	57.0% (53.7-60.3)
More than 10 years	60.5% (58.1-62.8)	60.1% (58.2-62.0)	60.2% (58.7-61.7)	53.0% (48.6-57.4)	58.1% (56.9-59.3)	57.8% (56.8-58.8)
Healthcare setting (Recoded)
Outpatient care	58.3% (54.8-61.7)	58.2% (56.0-60.4)	58.2% (56.5-59.9)	66.7% (56.3-76.0)	59.2% (57.2-61.1)	59.4% (57.5-61.2)
In-hospital care	59.6% (55.7-63.4)	63.1% (60.3-65.9)	61.8% (59.6-64.0)	57.0% (47.5-66.1)	57.8% (55.8-59.8)	57.8% (55.8-59.8)
Emergencies	57.6% (53.0-62.0)	62.2% (57.0-67.2)	60.2% (56.7-63.6)	51.4% (48.6-54.1)	56.9% (55.2-58.5)	55.4% (53.9-56.9)
Proximity to combat areas and occupied territories
No conflict zone	58.0% (55.4-60.5)	59.7% (57.5-61.9)	59.2% (57.6-60.8)	55.4% (50.8-60.0)	58.2% (56.9-59.6)	58.0% (56.9-59.2)
Conflict zone	63.2% (52.5-73.1)	60.5% (57.4-63.4)	61.1% (56.8-65.3)	50.8% (40.4-61.0)	58.3% (56.6-60.0)	57.8% (56.2-59.3)
Weighted Prevalence (95% CI)

tbl_bullying_w

Bullying
	Doctor			Nurse
	Male (N = 2,218)	Female (N = 6,670)	Overall (N = 8,888)	Male (N = 710)	Female (N = 10,717)	Overall (N = 11,427)
Overall
Overall	24.5% (21.6-27.5)	27.4% (26.0-28.7)	26.6% (25.3-27.9)	22.0% (18.6-25.7)	18.1% (16.6-19.6)	18.3% (17.0-19.7)
Age groups (Recoded)
20-30	54.9% (48.6-61.2)	60.0% (57.1-62.9)	58.4% (55.8-61.0)	57.0% (50.3-63.5)	58.7% (55.8-61.6)	58.4% (55.6-61.2)
31-45	53.0% (48.9-57.1)	54.8% (52.7-57.0)	54.4% (52.7-56.0)	48.8% (41.1-56.6)	54.5% (52.9-56.1)	54.2% (52.5-55.8)
46-55	60.7% (57.3-64.1)	60.3% (57.2-63.4)	60.4% (58.0-62.9)	56.5% (47.6-65.1)	59.5% (57.9-61.2)	59.4% (58.0-60.7)
Over 55	65.8% (62.1-69.4)	65.0% (62.3-67.5)	65.2% (62.9-67.4)	58.2% (46.4-69.4)	64.4% (62.1-66.7)	64.1% (61.9-66.2)
Relationship status
No partner	54.1% (48.9-59.3)	60.0% (57.0-63.0)	58.8% (56.0-61.5)	54.9% (48.2-61.5)	58.0% (55.9-60.1)	57.7% (55.7-59.8)
Has a partner	60.4% (58.2-62.5)	59.7% (57.9-61.5)	59.9% (58.6-61.2)	54.4% (49.5-59.2)	58.4% (57.1-59.7)	58.1% (56.9-59.3)
Having children in the household
Has children	60.0% (57.7-62.3)	60.0% (57.9-62.0)	60.0% (58.5-61.5)	54.0% (48.7-59.2)	57.9% (56.5-59.3)	57.7% (56.4-59.0)
No children	56.7% (52.5-60.8)	59.5% (56.9-62.1)	58.7% (56.5-60.8)	55.4% (51.0-59.8)	59.3% (57.6-60.9)	58.8% (57.1-60.5)
Post-graduate training
No	59.2% (56.7-61.7)	60.0% (58.1-61.9)	59.8% (58.4-61.1)	54.0% (49.7-58.4)	58.7% (57.5-60.0)	58.4% (57.4-59.4)
Yes	55.0% (48.7-61.2)	58.4% (53.3-63.3)	57.2% (52.9-61.6)	57.7% (43.9-70.8)	52.2% (47.8-56.5)	52.8% (49.0-56.7)
Length of service (Recoded)
5 years or less	57.7% (52.6-62.8)	61.6% (58.4-64.7)	60.5% (58.0-62.9)	61.7% (51.7-71.0)	60.6% (56.6-64.4)	60.8% (56.6-64.9)
6-10 years	50.7% (44.1-57.2)	55.7% (50.3-61.0)	54.2% (49.2-59.1)	51.4% (35.6-66.9)	57.6% (54.2-61.0)	57.0% (53.7-60.3)
More than 10 years	60.5% (58.1-62.8)	60.1% (58.2-62.0)	60.2% (58.7-61.7)	53.0% (48.6-57.4)	58.1% (56.9-59.3)	57.8% (56.8-58.8)
Healthcare setting (Recoded)
Outpatient care	58.3% (54.8-61.7)	58.2% (56.0-60.4)	58.2% (56.5-59.9)	66.7% (56.3-76.0)	59.2% (57.2-61.1)	59.4% (57.5-61.2)
In-hospital care	59.6% (55.7-63.4)	63.1% (60.3-65.9)	61.8% (59.6-64.0)	57.0% (47.5-66.1)	57.8% (55.8-59.8)	57.8% (55.8-59.8)
Emergencies	57.6% (53.0-62.0)	62.2% (57.0-67.2)	60.2% (56.7-63.6)	51.4% (48.6-54.1)	56.9% (55.2-58.5)	55.4% (53.9-56.9)
Proximity to combat areas and occupied territories
No conflict zone	58.0% (55.4-60.5)	59.7% (57.5-61.9)	59.2% (57.6-60.8)	55.4% (50.8-60.0)	58.2% (56.9-59.6)	58.0% (56.9-59.2)
Conflict zone	63.2% (52.5-73.1)	60.5% (57.4-63.4)	61.1% (56.8-65.3)	50.8% (40.4-61.0)	58.3% (56.6-60.0)	57.8% (56.2-59.3)
Weighted Prevalence (95% CI)

tbl_colleagues_w

Colleague support
	Doctor			Nurse
	Male (N = 2,259)	Female (N = 6,804)	Overall (N = 9,063)	Male (N = 729)	Female (N = 11,007)	Overall (N = 11,736)
Overall
Overall	58.7% (56.3-61.1)	59.8% (58.0-61.6)	59.5% (58.1-60.9)	54.6% (50.7-58.5)	58.3% (57.2-59.3)	58.0% (57.0-59.0)
Age groups (Recoded)
20-30	54.9% (48.6-61.2)	60.0% (57.1-62.9)	58.4% (55.8-61.0)	57.0% (50.3-63.5)	58.7% (55.8-61.6)	58.4% (55.6-61.2)
31-45	53.0% (48.9-57.1)	54.8% (52.7-57.0)	54.4% (52.7-56.0)	48.8% (41.1-56.6)	54.5% (52.9-56.1)	54.2% (52.5-55.8)
46-55	60.7% (57.3-64.1)	60.3% (57.2-63.4)	60.4% (58.0-62.9)	56.5% (47.6-65.1)	59.5% (57.9-61.2)	59.4% (58.0-60.7)
Over 55	65.8% (62.1-69.4)	65.0% (62.3-67.5)	65.2% (62.9-67.4)	58.2% (46.4-69.4)	64.4% (62.1-66.7)	64.1% (61.9-66.2)
Relationship status
No partner	54.1% (48.9-59.3)	60.0% (57.0-63.0)	58.8% (56.0-61.5)	54.9% (48.2-61.5)	58.0% (55.9-60.1)	57.7% (55.7-59.8)
Has a partner	60.4% (58.2-62.5)	59.7% (57.9-61.5)	59.9% (58.6-61.2)	54.4% (49.5-59.2)	58.4% (57.1-59.7)	58.1% (56.9-59.3)
Having children in the household
Has children	60.0% (57.7-62.3)	60.0% (57.9-62.0)	60.0% (58.5-61.5)	54.0% (48.7-59.2)	57.9% (56.5-59.3)	57.7% (56.4-59.0)
No children	56.7% (52.5-60.8)	59.5% (56.9-62.1)	58.7% (56.5-60.8)	55.4% (51.0-59.8)	59.3% (57.6-60.9)	58.8% (57.1-60.5)
Post-graduate training
No	59.2% (56.7-61.7)	60.0% (58.1-61.9)	59.8% (58.4-61.1)	54.0% (49.7-58.4)	58.7% (57.5-60.0)	58.4% (57.4-59.4)
Yes	55.0% (48.7-61.2)	58.4% (53.3-63.3)	57.2% (52.9-61.6)	57.7% (43.9-70.8)	52.2% (47.8-56.5)	52.8% (49.0-56.7)
Length of service (Recoded)
5 years or less	57.7% (52.6-62.8)	61.6% (58.4-64.7)	60.5% (58.0-62.9)	61.7% (51.7-71.0)	60.6% (56.6-64.4)	60.8% (56.6-64.9)
6-10 years	50.7% (44.1-57.2)	55.7% (50.3-61.0)	54.2% (49.2-59.1)	51.4% (35.6-66.9)	57.6% (54.2-61.0)	57.0% (53.7-60.3)
More than 10 years	60.5% (58.1-62.8)	60.1% (58.2-62.0)	60.2% (58.7-61.7)	53.0% (48.6-57.4)	58.1% (56.9-59.3)	57.8% (56.8-58.8)
Healthcare setting (Recoded)
Outpatient care	58.3% (54.8-61.7)	58.2% (56.0-60.4)	58.2% (56.5-59.9)	66.7% (56.3-76.0)	59.2% (57.2-61.1)	59.4% (57.5-61.2)
In-hospital care	59.6% (55.7-63.4)	63.1% (60.3-65.9)	61.8% (59.6-64.0)	57.0% (47.5-66.1)	57.8% (55.8-59.8)	57.8% (55.8-59.8)
Emergencies	57.6% (53.0-62.0)	62.2% (57.0-67.2)	60.2% (56.7-63.6)	51.4% (48.6-54.1)	56.9% (55.2-58.5)	55.4% (53.9-56.9)
Proximity to combat areas and occupied territories
No conflict zone	58.0% (55.4-60.5)	59.7% (57.5-61.9)	59.2% (57.6-60.8)	55.4% (50.8-60.0)	58.2% (56.9-59.6)	58.0% (56.9-59.2)
Conflict zone	63.2% (52.5-73.1)	60.5% (57.4-63.4)	61.1% (56.8-65.3)	50.8% (40.4-61.0)	58.3% (56.6-60.0)	57.8% (56.2-59.3)
Weighted Prevalence (95% CI)

tbl_superiors_w

Support from superiors
	Doctor			Nurse
	Male (N = 2,255)	Female (N = 6,793)	Overall (N = 9,048)	Male (N = 729)	Female (N = 10,974)	Overall (N = 11,703)
Overall
Overall	49.5% (47.4-51.6)	52.7% (50.4-55.1)	51.8% (49.9-53.8)	48.7% (44.6-52.8)	51.2% (49.3-53.1)	51.1% (49.3-52.8)
Age groups (Recoded)
20-30	54.9% (48.6-61.2)	60.0% (57.1-62.9)	58.4% (55.8-61.0)	57.0% (50.3-63.5)	58.7% (55.8-61.6)	58.4% (55.6-61.2)
31-45	53.0% (48.9-57.1)	54.8% (52.7-57.0)	54.4% (52.7-56.0)	48.8% (41.1-56.6)	54.5% (52.9-56.1)	54.2% (52.5-55.8)
46-55	60.7% (57.3-64.1)	60.3% (57.2-63.4)	60.4% (58.0-62.9)	56.5% (47.6-65.1)	59.5% (57.9-61.2)	59.4% (58.0-60.7)
Over 55	65.8% (62.1-69.4)	65.0% (62.3-67.5)	65.2% (62.9-67.4)	58.2% (46.4-69.4)	64.4% (62.1-66.7)	64.1% (61.9-66.2)
Relationship status
No partner	54.1% (48.9-59.3)	60.0% (57.0-63.0)	58.8% (56.0-61.5)	54.9% (48.2-61.5)	58.0% (55.9-60.1)	57.7% (55.7-59.8)
Has a partner	60.4% (58.2-62.5)	59.7% (57.9-61.5)	59.9% (58.6-61.2)	54.4% (49.5-59.2)	58.4% (57.1-59.7)	58.1% (56.9-59.3)
Having children in the household
Has children	60.0% (57.7-62.3)	60.0% (57.9-62.0)	60.0% (58.5-61.5)	54.0% (48.7-59.2)	57.9% (56.5-59.3)	57.7% (56.4-59.0)
No children	56.7% (52.5-60.8)	59.5% (56.9-62.1)	58.7% (56.5-60.8)	55.4% (51.0-59.8)	59.3% (57.6-60.9)	58.8% (57.1-60.5)
Post-graduate training
No	59.2% (56.7-61.7)	60.0% (58.1-61.9)	59.8% (58.4-61.1)	54.0% (49.7-58.4)	58.7% (57.5-60.0)	58.4% (57.4-59.4)
Yes	55.0% (48.7-61.2)	58.4% (53.3-63.3)	57.2% (52.9-61.6)	57.7% (43.9-70.8)	52.2% (47.8-56.5)	52.8% (49.0-56.7)
Length of service (Recoded)
5 years or less	57.7% (52.6-62.8)	61.6% (58.4-64.7)	60.5% (58.0-62.9)	61.7% (51.7-71.0)	60.6% (56.6-64.4)	60.8% (56.6-64.9)
6-10 years	50.7% (44.1-57.2)	55.7% (50.3-61.0)	54.2% (49.2-59.1)	51.4% (35.6-66.9)	57.6% (54.2-61.0)	57.0% (53.7-60.3)
More than 10 years	60.5% (58.1-62.8)	60.1% (58.2-62.0)	60.2% (58.7-61.7)	53.0% (48.6-57.4)	58.1% (56.9-59.3)	57.8% (56.8-58.8)
Healthcare setting (Recoded)
Outpatient care	58.3% (54.8-61.7)	58.2% (56.0-60.4)	58.2% (56.5-59.9)	66.7% (56.3-76.0)	59.2% (57.2-61.1)	59.4% (57.5-61.2)
In-hospital care	59.6% (55.7-63.4)	63.1% (60.3-65.9)	61.8% (59.6-64.0)	57.0% (47.5-66.1)	57.8% (55.8-59.8)	57.8% (55.8-59.8)
Emergencies	57.6% (53.0-62.0)	62.2% (57.0-67.2)	60.2% (56.7-63.6)	51.4% (48.6-54.1)	56.9% (55.2-58.5)	55.4% (53.9-56.9)
Proximity to combat areas and occupied territories
No conflict zone	58.0% (55.4-60.5)	59.7% (57.5-61.9)	59.2% (57.6-60.8)	55.4% (50.8-60.0)	58.2% (56.9-59.6)	58.0% (56.9-59.2)
Conflict zone	63.2% (52.5-73.1)	60.5% (57.4-63.4)	61.1% (56.8-65.3)	50.8% (40.4-61.0)	58.3% (56.6-60.0)	57.8% (56.2-59.3)
Weighted Prevalence (95% CI)

tbl_meaning_w

Meaning in work
	Doctor			Nurse
	Male (N = 1,718)	Female (N = 5,289)	Overall (N = 7,007)	Male (N = 525)	Female (N = 8,186)	Overall (N = 8,711)
Overall
Overall	81.3% (78.7-83.6)	84.5% (82.6-86.2)	83.6% (81.8-85.2)	68.6% (64.7-72.3)	80.6% (79.4-81.8)	79.8% (78.7-80.9)
Age groups (Recoded)
20-30	54.9% (48.6-61.2)	60.0% (57.1-62.9)	58.4% (55.8-61.0)	57.0% (50.3-63.5)	58.7% (55.8-61.6)	58.4% (55.6-61.2)
31-45	53.0% (48.9-57.1)	54.8% (52.7-57.0)	54.4% (52.7-56.0)	48.8% (41.1-56.6)	54.5% (52.9-56.1)	54.2% (52.5-55.8)
46-55	60.7% (57.3-64.1)	60.3% (57.2-63.4)	60.4% (58.0-62.9)	56.5% (47.6-65.1)	59.5% (57.9-61.2)	59.4% (58.0-60.7)
Over 55	65.8% (62.1-69.4)	65.0% (62.3-67.5)	65.2% (62.9-67.4)	58.2% (46.4-69.4)	64.4% (62.1-66.7)	64.1% (61.9-66.2)
Relationship status
No partner	54.1% (48.9-59.3)	60.0% (57.0-63.0)	58.8% (56.0-61.5)	54.9% (48.2-61.5)	58.0% (55.9-60.1)	57.7% (55.7-59.8)
Has a partner	60.4% (58.2-62.5)	59.7% (57.9-61.5)	59.9% (58.6-61.2)	54.4% (49.5-59.2)	58.4% (57.1-59.7)	58.1% (56.9-59.3)
Having children in the household
Has children	60.0% (57.7-62.3)	60.0% (57.9-62.0)	60.0% (58.5-61.5)	54.0% (48.7-59.2)	57.9% (56.5-59.3)	57.7% (56.4-59.0)
No children	56.7% (52.5-60.8)	59.5% (56.9-62.1)	58.7% (56.5-60.8)	55.4% (51.0-59.8)	59.3% (57.6-60.9)	58.8% (57.1-60.5)
Post-graduate training
No	59.2% (56.7-61.7)	60.0% (58.1-61.9)	59.8% (58.4-61.1)	54.0% (49.7-58.4)	58.7% (57.5-60.0)	58.4% (57.4-59.4)
Yes	55.0% (48.7-61.2)	58.4% (53.3-63.3)	57.2% (52.9-61.6)	57.7% (43.9-70.8)	52.2% (47.8-56.5)	52.8% (49.0-56.7)
Length of service (Recoded)
5 years or less	57.7% (52.6-62.8)	61.6% (58.4-64.7)	60.5% (58.0-62.9)	61.7% (51.7-71.0)	60.6% (56.6-64.4)	60.8% (56.6-64.9)
6-10 years	50.7% (44.1-57.2)	55.7% (50.3-61.0)	54.2% (49.2-59.1)	51.4% (35.6-66.9)	57.6% (54.2-61.0)	57.0% (53.7-60.3)
More than 10 years	60.5% (58.1-62.8)	60.1% (58.2-62.0)	60.2% (58.7-61.7)	53.0% (48.6-57.4)	58.1% (56.9-59.3)	57.8% (56.8-58.8)
Healthcare setting (Recoded)
Outpatient care	58.3% (54.8-61.7)	58.2% (56.0-60.4)	58.2% (56.5-59.9)	66.7% (56.3-76.0)	59.2% (57.2-61.1)	59.4% (57.5-61.2)
In-hospital care	59.6% (55.7-63.4)	63.1% (60.3-65.9)	61.8% (59.6-64.0)	57.0% (47.5-66.1)	57.8% (55.8-59.8)	57.8% (55.8-59.8)
Emergencies	57.6% (53.0-62.0)	62.2% (57.0-67.2)	60.2% (56.7-63.6)	51.4% (48.6-54.1)	56.9% (55.2-58.5)	55.4% (53.9-56.9)
Proximity to combat areas and occupied territories
No conflict zone	58.0% (55.4-60.5)	59.7% (57.5-61.9)	59.2% (57.6-60.8)	55.4% (50.8-60.0)	58.2% (56.9-59.6)	58.0% (56.9-59.2)
Conflict zone	63.2% (52.5-73.1)	60.5% (57.4-63.4)	61.1% (56.8-65.3)	50.8% (40.4-61.0)	58.3% (56.6-60.0)	57.8% (56.2-59.3)
Weighted Prevalence (95% CI)

tbl_purpose_w

Purpose in work
	Doctor			Nurse
	Male (N = 1,806)	Female (N = 5,558)	Overall (N = 7,364)	Male (N = 587)	Female (N = 8,906)	Overall (N = 9,493)
Overall
Overall	87.7% (85.7-89.4)	90.7% (89.5-91.8)	89.9% (88.7-91.0)	84.7% (81.8-87.2)	89.2% (88.4-90.0)	88.9% (88.1-89.7)
Age groups (Recoded)
20-30	54.9% (48.6-61.2)	60.0% (57.1-62.9)	58.4% (55.8-61.0)	57.0% (50.3-63.5)	58.7% (55.8-61.6)	58.4% (55.6-61.2)
31-45	53.0% (48.9-57.1)	54.8% (52.7-57.0)	54.4% (52.7-56.0)	48.8% (41.1-56.6)	54.5% (52.9-56.1)	54.2% (52.5-55.8)
46-55	60.7% (57.3-64.1)	60.3% (57.2-63.4)	60.4% (58.0-62.9)	56.5% (47.6-65.1)	59.5% (57.9-61.2)	59.4% (58.0-60.7)
Over 55	65.8% (62.1-69.4)	65.0% (62.3-67.5)	65.2% (62.9-67.4)	58.2% (46.4-69.4)	64.4% (62.1-66.7)	64.1% (61.9-66.2)
Relationship status
No partner	54.1% (48.9-59.3)	60.0% (57.0-63.0)	58.8% (56.0-61.5)	54.9% (48.2-61.5)	58.0% (55.9-60.1)	57.7% (55.7-59.8)
Has a partner	60.4% (58.2-62.5)	59.7% (57.9-61.5)	59.9% (58.6-61.2)	54.4% (49.5-59.2)	58.4% (57.1-59.7)	58.1% (56.9-59.3)
Having children in the household
Has children	60.0% (57.7-62.3)	60.0% (57.9-62.0)	60.0% (58.5-61.5)	54.0% (48.7-59.2)	57.9% (56.5-59.3)	57.7% (56.4-59.0)
No children	56.7% (52.5-60.8)	59.5% (56.9-62.1)	58.7% (56.5-60.8)	55.4% (51.0-59.8)	59.3% (57.6-60.9)	58.8% (57.1-60.5)
Post-graduate training
No	59.2% (56.7-61.7)	60.0% (58.1-61.9)	59.8% (58.4-61.1)	54.0% (49.7-58.4)	58.7% (57.5-60.0)	58.4% (57.4-59.4)
Yes	55.0% (48.7-61.2)	58.4% (53.3-63.3)	57.2% (52.9-61.6)	57.7% (43.9-70.8)	52.2% (47.8-56.5)	52.8% (49.0-56.7)
Length of service (Recoded)
5 years or less	57.7% (52.6-62.8)	61.6% (58.4-64.7)	60.5% (58.0-62.9)	61.7% (51.7-71.0)	60.6% (56.6-64.4)	60.8% (56.6-64.9)
6-10 years	50.7% (44.1-57.2)	55.7% (50.3-61.0)	54.2% (49.2-59.1)	51.4% (35.6-66.9)	57.6% (54.2-61.0)	57.0% (53.7-60.3)
More than 10 years	60.5% (58.1-62.8)	60.1% (58.2-62.0)	60.2% (58.7-61.7)	53.0% (48.6-57.4)	58.1% (56.9-59.3)	57.8% (56.8-58.8)
Healthcare setting (Recoded)
Outpatient care	58.3% (54.8-61.7)	58.2% (56.0-60.4)	58.2% (56.5-59.9)	66.7% (56.3-76.0)	59.2% (57.2-61.1)	59.4% (57.5-61.2)
In-hospital care	59.6% (55.7-63.4)	63.1% (60.3-65.9)	61.8% (59.6-64.0)	57.0% (47.5-66.1)	57.8% (55.8-59.8)	57.8% (55.8-59.8)
Emergencies	57.6% (53.0-62.0)	62.2% (57.0-67.2)	60.2% (56.7-63.6)	51.4% (48.6-54.1)	56.9% (55.2-58.5)	55.4% (53.9-56.9)
Proximity to combat areas and occupied territories
No conflict zone	58.0% (55.4-60.5)	59.7% (57.5-61.9)	59.2% (57.6-60.8)	55.4% (50.8-60.0)	58.2% (56.9-59.6)	58.0% (56.9-59.2)
Conflict zone	63.2% (52.5-73.1)	60.5% (57.4-63.4)	61.1% (56.8-65.3)	50.8% (40.4-61.0)	58.3% (56.6-60.0)	57.8% (56.2-59.3)
Weighted Prevalence (95% CI)

Exposures

# Generate table
table_all_exposures_w <- gen_w_exp_tbl(exposures)

gtsave(table_all_exposures_w, "out/tables/all_exp_tbl_w.docx")

table_all_exposures_w

Exposure	Doctor			Nurse
	Female (N = 7,662)	Male (N = 2,496)	Overall (N = 10,158)	Female (N = 12,744)	Male (N = 835)	Overall (N = 13,579)
Risk factor
Bullying	27.4% (26.0-28.7)	24.5% (21.6-27.5)	26.6% (25.3-27.9)	18.1% (16.6-19.6)	22.0% (18.6-25.7)	18.3% (17.0-19.7)
Sexual Harassment	9.8% (9.0-10.5)	8.2% (7.2-9.3)	9.3% (8.7-10.0)	6.8% (6.0-7.6)	8.8% (7.1-10.7)	6.9% (6.1-7.7)
Violent threats	17.6% (15.6-19.8)	27.2% (24.0-30.6)	20.3% (18.3-22.4)	10.2% (8.3-12.5)	29.0% (23.1-35.4)	11.6% (9.2-14.4)
Physical violence	2.5% (2.0-3.1)	5.5% (4.0-7.4)	3.3% (2.7-4.1)	3.1% (2.5-3.8)	10.9% (8.1-14.4)	3.7% (2.9-4.6)
Protective factor
Colleague support	59.8% (58.0-61.6)	58.7% (56.3-61.1)	59.5% (58.1-60.9)	58.3% (57.2-59.3)	54.6% (50.7-58.5)	58.0% (57.0-59.0)
Support from superiors	52.7% (50.4-55.1)	49.5% (47.4-51.6)	51.8% (49.9-53.8)	51.2% (49.3-53.1)	48.7% (44.6-52.8)	51.1% (49.3-52.8)
Meaning in work	84.5% (82.6-86.2)	81.3% (78.7-83.6)	83.6% (81.8-85.2)	80.6% (79.4-81.8)	68.6% (64.7-72.3)	79.8% (78.7-80.9)
Purpose in work	90.7% (89.5-91.8)	87.7% (85.7-89.4)	89.9% (88.7-91.0)	89.2% (88.4-90.0)	84.7% (81.8-87.2)	88.9% (88.1-89.7)
Weighted Prevalence (95% CI)

Outcomes

# Generate table
table_all_outcomes_w <- gen_w_out_tbl(outcomes)

gtsave(table_all_outcomes_w, "out/tables/all_out_tbl_w.docx")

table_all_outcomes_w

Outcome	Doctor			Nurse
	Male (N = 2,496)	Female (N = 7,662)	Overall (N = 10,158)	Male (N = 835)	Female (N = 12,744)	Overall (N = 13,579)
Probable Depression (PHQ-9)	33.8% (30.9-36.8)	35.5% (33.4-37.7)	35.1% (33.1-37.1)	27.5% (22.8-32.5)	22.4% (20.1-24.9)	22.8% (20.4-25.3)
Probable Anxiety (GAD-7)	21.7% (19.6-23.9)	25.7% (24.0-27.4)	24.6% (23.1-26.1)	16.3% (12.0-21.5)	16.6% (14.7-18.6)	16.6% (14.7-18.5)
Passive suicide thoughts (Item 9 of PHQ-9)	15.2% (13.3-17.2)	11.4% (10.5-12.3)	12.4% (11.5-13.4)	12.6% (9.1-16.9)	8.0% (6.8-9.3)	8.3% (7.1-9.7)
Unweighted Prevalence (95% CI). PHQ-9: 9-item Patient Health Questionnaire, a cut-off score ≥ 10 is defined for detecting probable depression. GAD-7: 7 item anxiety scale, a cut-off score ≥ 10 is defined for detecting probable anxiety. Any positive answer in the 9th item of the PHQ-9 is considered as having passive suicide thoughts.

Svy Models

# generate the grid for all possible combinations
tasks_grid <- expand_grid(
  outcome = outcomes,
  exposure = names(exposure_map),
  profession = c("Doctor", "Nurse")
)

# run function 
pwalk(
  list(tasks_grid$outcome, 
       tasks_grid$exposure, 
       tasks_grid$profession),
  function(out, exp, prof) {
    message(glue("Running: {out} vs {exp} ({prof})..."))
    run_svy_models(out, exp, prof, svy_ua_w)
  }
)

## Running: phq_co vs work_21_dic (Doctor)...

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## Running: phq_co vs work_22_dic (Doctor)...

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## Running: phq_co vs work_30_dic (Doctor)...

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## Running: phq_co vs wb_wami_1_dic (Doctor)...

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## Running: phq_co vs wb_wami_2_dic (Doctor)...

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## Running: gad_co vs work_21_dic (Doctor)...

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## Running: gad_co vs work_22_dic (Doctor)...

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## Running: gad_co vs wb_wami_1_dic (Doctor)...

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## Running: gad_co vs wb_wami_2_dic (Doctor)...

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## Running: suic_idea vs work_21_dic (Doctor)...

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## Running: suic_idea vs work_22_dic (Doctor)...

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## Running: suic_idea vs work_30_dic (Doctor)...

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## Running: suic_idea vs work_33_dic (Doctor)...

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## Running: suic_idea vs work_31_dic (Doctor)...

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## Running: suic_idea vs work_32_dic (Doctor)...

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## Running: suic_idea vs work_32_dic (Nurse)...

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## Running: suic_idea vs wb_wami_1_dic (Doctor)...

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## Running: suic_idea vs wb_wami_2_dic (Doctor)...

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Survey results table

# find svy objects
svy_model_names <- ls(pattern = "^svy_", envir = .GlobalEnv)

# Extraction
survey_results_df <- 
  map_dfr(svy_model_names, function(obj_name) {
  
  # Load the model object
  model <- get(obj_name, envir = .GlobalEnv)
  
  # decode filename
  parts <- str_split(obj_name, "_")[[1]]
  
  out_code   <- parts[2]
  exp_code   <- parts[3]
  prof_code  <- parts[4]
  type_code  <- parts[5]
  
  # Decode Labels for the Table
  outcome_label <- case_when(
    out_code == "dep"  ~ "Depression",
    out_code == "anx"  ~ "Anxiety",
    out_code == "suic" ~ "Suicide thoughts"
  )
  
  exposure_label <- case_when(
    exp_code == "cols"    ~ "Colleague support",
    exp_code == "sup"     ~ "Support from superiors",
    exp_code == "har"     ~ "Sexual Harassment",
    exp_code == "bul"     ~ "Bullying",
    exp_code == "threats" ~ "Violent threats",
    exp_code == "viol"    ~ "Physical violence",
    exp_code == "mean"    ~ "Meaning in work",
    exp_code == "purp"    ~ "Purpose in work"
  )
  
  profession_label <- ifelse(prof_code == "doc", "Doctor", "Nurse")
  
  model_label <- case_when(
    type_code == "cr"  ~ "Crude",
    type_code == "str" ~ "Partially",
    type_code == "adj" ~ "Fully Adj."
  )
  
 
  term_lookup <- case_when(
    exp_code == "cols"    ~ "work_21_dicYes",
    exp_code == "sup"     ~ "work_22_dicYes",
    exp_code == "har"     ~ "work_30_dicYes",
    exp_code == "bul"     ~ "work_33_dicYes",
    exp_code == "threats" ~ "work_31_dicYes",
    exp_code == "viol"    ~ "work_32_dicYes",
    exp_code == "mean"    ~ "wb_wami_1_dicYes",
    exp_code == "purp"    ~ "wb_wami_2_dicYes"
  )
  
  # extract stats
  res <- tryCatch({
    
    # Check family
    is_logistic <- model$family$family %in% c("quasipoisson", "binomial")
    
    # Try to tidy
    tidy(model, conf.int = TRUE, exponentiate = is_logistic)
    
  }, error = function(e) {
    # If it fails, print the culprit and return NULL
    warning(paste("FAILED model:", obj_name, "-", e$message))
    return(NULL)
  })
  
  if (is.null(res)) return(NULL)
  
  # exposure term
  target_row <- 
    res |> 
    filter(term == term_lookup)
  
  if(nrow(target_row) > 0) {
    # If multiple rows match (rare), take the first one
    est  <- target_row$estimate[1]
    low  <- target_row$conf.low[1]
    high <- target_row$conf.high[1]
    
    
    # calculate absolute risks/means
    dat <- model$model
    
    exp_var_name <- str_remove(term_lookup, "Yes$")
    resp_var_name <- names(dat)[1]
    
    x_vec <- as.character(dat[[exp_var_name]])
    y_raw <- as.numeric(dat[[resp_var_name]])
    
    if (is.factor(y_raw)) {
       # Ensure it is 0 and 1
       y_vec <- as.numeric(as.character(y_raw)) 
    } else {
       y_vec <- as.numeric(y_raw)
    }
    
    if (is_logistic) {
      # Normalize 1/2 to 0/1 if necessary
      if(max(y_vec, na.rm = TRUE) > 1) y_vec <- y_vec - 1
      
      # Calculate %
      risk_unexp <- mean(y_vec[x_vec == "No"], na.rm = TRUE) * 100
      risk_exp   <- mean(y_vec[x_vec == "Yes"], na.rm = TRUE) * 100
      
      risk_unexp_str <- sprintf("%.1f%%", risk_unexp)
      risk_exp_str   <- sprintf("%.1f%%", risk_exp)
      
      } else {
        
        # Calculate Mean
        mean_unexp <- mean(y_vec[x_vec == "No"], na.rm = TRUE)
        mean_exp   <- mean(y_vec[x_vec == "Yes"], na.rm = TRUE)
        
        risk_unexp_str <- sprintf("%.2f", mean_unexp)
        risk_exp_str   <- sprintf("%.2f", mean_exp)
        }
    
    tibble(
      Outcome = outcome_label,
      Exposure = exposure_label,
      Profession = profession_label,
      Model = model_label,
      Type = ifelse(is_logistic, 
                    "PR", 
                    "Beta"),
      Est_CI = sprintf("%.2f (%.2f, %.2f)", est, low, high),
      Risk_Unexposed = risk_unexp_str,
      Risk_Exposed = risk_exp_str
    )
    
  } else {
    return(NULL)
  }
})


survey_results_df

## # A tibble: 144 × 8
##    Outcome Exposure    Profession Model Type  Est_CI Risk_Unexposed Risk_Exposed
##    <chr>   <chr>       <chr>      <chr> <chr> <chr>  <chr>          <chr>       
##  1 Anxiety Bullying    Doctor     Full… PR    2.11 … 14.9%          38.9%       
##  2 Anxiety Bullying    Doctor     Crude PR    2.33 … 14.8%          38.9%       
##  3 Anxiety Bullying    Doctor     Part… PR    2.10 … 14.8%          38.9%       
##  4 Anxiety Bullying    Nurse      Full… PR    2.69 … 14.9%          38.9%       
##  5 Anxiety Bullying    Nurse      Crude PR    2.82 … 14.8%          38.9%       
##  6 Anxiety Bullying    Nurse      Part… PR    2.68 … 14.8%          38.9%       
##  7 Anxiety Colleague … Doctor     Full… PR    0.65 … 26.4%          15.9%       
##  8 Anxiety Colleague … Doctor     Crude PR    0.62 … 26.3%          15.8%       
##  9 Anxiety Colleague … Doctor     Part… PR    0.65 … 26.3%          15.8%       
## 10 Anxiety Colleague … Nurse      Full… PR    0.58 … 26.4%          15.9%       
## # ℹ 134 more rows

# Save
saveRDS(survey_results_df, "out/dataframes/svy_results_df.rds")
writexl::write_xlsx(survey_results_df, "out/dataframes/svy_results_df.xlsx")

# generate table
tbl_svy_res <- build_svy_gt(
  survey_results_df, 
  metric_type = "PR", 
  title_text = "**Survey Logistic Models (PR)**", 
  footnote_text = "Weighted Prevalence Ratios (95% CI)"
)


gtsave(tbl_svy_res, "out/tables/svy_res_tbl.docx")


tbl_svy_res

Survey Logistic Models (PR)
	Doctor			Nurse
	Crude1	Partially adjusted2	Fully adjusted3	Crude1	Partially adjusted2	Fully adjusted3
Depression
Bullying	2.23 (2.13, 2.33)	2.03 (1.93, 2.13)	2.03 (1.93, 2.14)	2.69 (2.45, 2.96)	2.53 (2.26, 2.83)	2.53 (2.26, 2.83)
Sexual Harassment	1.78 (1.66, 1.90)	1.48 (1.38, 1.60)	1.49 (1.39, 1.61)	2.41 (2.21, 2.63)	2.10 (1.90, 2.33)	2.11 (1.91, 2.33)
Violent threats	2.00 (1.89, 2.12)	1.85 (1.74, 1.97)	1.86 (1.75, 1.98)	2.33 (2.16, 2.51)	2.17 (2.01, 2.33)	2.17 (2.05, 2.31)
Physical violence	1.67 (1.46, 1.91)	1.49 (1.30, 1.71)	1.50 (1.30, 1.72)	2.30 (2.07, 2.56)	2.09 (1.86, 2.35)	2.08 (1.87, 2.33)
Colleague support	0.63 (0.59, 0.67)	0.66 (0.61, 0.71)	0.66 (0.61, 0.71)	0.58 (0.55, 0.62)	0.60 (0.56, 0.64)	0.60 (0.55, 0.64)
Support from superiors	0.58 (0.54, 0.63)	0.62 (0.57, 0.67)	0.62 (0.57, 0.67)	0.50 (0.46, 0.55)	0.52 (0.48, 0.57)	0.52 (0.47, 0.57)
Meaning in work	0.48 (0.45, 0.51)	0.50 (0.47, 0.53)	0.50 (0.47, 0.53)	0.42 (0.38, 0.46)	0.44 (0.40, 0.48)	0.44 (0.40, 0.49)
Purpose in work	0.45 (0.42, 0.48)	0.48 (0.45, 0.51)	0.48 (0.45, 0.51)	0.46 (0.43, 0.49)	0.48 (0.45, 0.52)	0.49 (0.45, 0.53)
Anxiety
Bullying	2.33 (2.18, 2.49)	2.10 (1.96, 2.26)	2.11 (1.96, 2.28)	2.82 (2.57, 3.09)	2.68 (2.42, 2.98)	2.69 (2.42, 2.99)
Sexual Harassment	1.96 (1.82, 2.11)	1.61 (1.48, 1.76)	1.62 (1.49, 1.77)	2.55 (2.21, 2.95)	2.29 (1.97, 2.65)	2.30 (1.99, 2.65)
Violent threats	2.06 (1.90, 2.23)	1.92 (1.77, 2.09)	1.94 (1.77, 2.11)	2.39 (2.17, 2.63)	2.30 (2.12, 2.50)	2.31 (2.17, 2.47)
Physical violence	1.93 (1.71, 2.19)	1.75 (1.56, 1.97)	1.81 (1.60, 2.04)	2.30 (1.93, 2.74)	2.17 (1.82, 2.57)	2.18 (1.84, 2.58)
Colleague support	0.62 (0.59, 0.66)	0.65 (0.61, 0.69)	0.65 (0.61, 0.69)	0.57 (0.52, 0.63)	0.58 (0.52, 0.64)	0.58 (0.52, 0.64)
Support from superiors	0.56 (0.52, 0.61)	0.60 (0.55, 0.64)	0.59 (0.55, 0.64)	0.50 (0.46, 0.55)	0.52 (0.47, 0.57)	0.51 (0.47, 0.57)
Meaning in work	0.47 (0.44, 0.51)	0.49 (0.45, 0.53)	0.49 (0.45, 0.54)	0.46 (0.41, 0.51)	0.47 (0.42, 0.53)	0.47 (0.42, 0.54)
Purpose in work	0.44 (0.40, 0.48)	0.46 (0.42, 0.51)	0.47 (0.42, 0.52)	0.49 (0.44, 0.55)	0.50 (0.44, 0.57)	0.51 (0.45, 0.58)
Suicide thoughts
Bullying	2.49 (2.22, 2.79)	2.22 (1.96, 2.51)	2.22 (1.95, 2.53)	3.47 (2.96, 4.07)	3.20 (2.72, 3.77)	3.17 (2.69, 3.74)
Sexual Harassment	2.62 (2.45, 2.80)	2.16 (2.00, 2.32)	2.15 (1.99, 2.32)	3.35 (2.81, 3.99)	2.83 (2.36, 3.40)	2.80 (2.34, 3.36)
Violent threats	2.48 (2.21, 2.79)	2.17 (1.93, 2.43)	2.18 (1.94, 2.44)	2.98 (2.52, 3.52)	2.67 (2.21, 3.23)	2.63 (2.18, 3.16)
Physical violence	2.83 (2.51, 3.19)	2.31 (2.04, 2.63)	2.35 (1.99, 2.77)	3.23 (2.60, 4.01)	2.77 (2.17, 3.54)	2.74 (2.13, 3.53)
Colleague support	0.60 (0.54, 0.67)	0.64 (0.57, 0.71)	0.64 (0.57, 0.71)	0.51 (0.44, 0.60)	0.52 (0.45, 0.61)	0.52 (0.45, 0.61)
Support from superiors	0.56 (0.49, 0.65)	0.61 (0.53, 0.70)	0.61 (0.53, 0.71)	0.54 (0.45, 0.63)	0.56 (0.47, 0.66)	0.55 (0.47, 0.65)
Meaning in work	0.40 (0.34, 0.47)	0.43 (0.37, 0.50)	0.44 (0.38, 0.51)	0.45 (0.39, 0.51)	0.47 (0.41, 0.54)	0.47 (0.41, 0.54)
Purpose in work	0.35 (0.30, 0.40)	0.38 (0.33, 0.45)	0.39 (0.34, 0.45)	0.41 (0.37, 0.47)	0.44 (0.39, 0.50)	0.44 (0.39, 0.50)
Weighted Prevalence Ratios (95% CI)
1 No covariates included.
2 Adjusted by Gender, Age group and Proximity to combat areas and occupied territories .
3 Partially adjusted models plus specific covariates: Bullying and sexual harassment adjusted by Healthcare setting, Post-graduate training and Length of service. Violent threats and physical violence adjusted by Healthcare setting. Support from colleagues or superiors adjusted by Healthcare setting and Length of service. Finding meaning or purpose in job adjusted by Healthcare setting, Relationship status, Having children, and Length of service.

ls() |> 
  str_subset("^svy_") |> 
  rm(list = _)

rm(build_svy_gt, run_svy_models)

Bayesian Approach

Define priors

get_prior(
  formula = phq_co ~ scdm_1_rec + scdm_2_rec + scdm_4_dic + scdm_5_dic + work_5_rec + work_3_rec + work_1 + work_2 + (1 | loc_3),  # The random effect for region
  data = ds_ua,
  family = poisson(link = "log")
)

##                    prior     class                      coef group resp dpar
##                   (flat)         b                                          
##                   (flat)         b           scdm_1_rec31M45                
##                   (flat)         b           scdm_1_rec46M55                
##                   (flat)         b          scdm_1_recOver55                
##                   (flat)         b            scdm_2_recMale                
##                   (flat)         b     scdm_4_dicHasapartner                
##                   (flat)         b      scdm_5_dicNochildren                
##                   (flat)         b                 work_1Yes                
##                   (flat)         b               work_2Nurse                
##                   (flat)         b       work_3_rec6M10years                
##                   (flat)         b work_3_recMorethan10years                
##                   (flat)         b     work_5_recEmergencies                
##                   (flat)         b work_5_recInMhospitalcare                
##  student_t(3, -2.3, 2.5) Intercept                                          
##     student_t(3, 0, 2.5)        sd                                          
##     student_t(3, 0, 2.5)        sd                           loc_3          
##     student_t(3, 0, 2.5)        sd                 Intercept loc_3          
##  nlpar lb ub tag       source
##                       default
##                  (vectorized)
##                  (vectorized)
##                  (vectorized)
##                  (vectorized)
##                  (vectorized)
##                  (vectorized)
##                  (vectorized)
##                  (vectorized)
##                  (vectorized)
##                  (vectorized)
##                  (vectorized)
##                  (vectorized)
##                       default
##         0             default
##         0        (vectorized)
##         0        (vectorized)

# set list of priors.

bin_priors <- c(
  # Intercept MUST be negative
  # Mean -1.5 is ~ 22% prevalence (exp(-1.5))
  # SD 1 allows range from -2.5 to -0.5 (~ 8 to 60%)
  # Prevents model crashing 
  prior(normal(-1.5, 1), class = Intercept),  # vague prior for intercept
  
  # 0.5 allows risk ratios up to ~ 2.7, which is realistic
  prior(normal(0, 0.5), class = b),          # weak prior for predictors
  prior(student_t(3, 0, 1), class = sd)    # weak prior for random effect variance
)



# use stan as backend engine, newer, lighter, faster than default rstan
options(brms.backend = "cmdstanr")
options(mc.cores = 4) # forces it to always run 4 simultaneous cores

Models

Aggregated

# define covariates and outcomes

outcomes_list_agg <- c("phq_co", "gad_co")

# for model 2 in each
covars_str_agg <- c("conflict", "scdm_1_rec", "scdm_2_rec", "work_2")

# specific

covars_harbul <- c("work_5_rec", "work_1", "work_3_rec")
  
covars_viol <- c("work_5_rec")

# assign covariates to each exposure
exposure_map_agg <- list(
  
  "work_30_dic" = covars_harbul,
  "work_33_dic" = covars_harbul,
  
  "work_31_dic" = covars_viol,
  "work_32_dic" = covars_viol
)

# Define outcome-exposure binomials for running function
brms_tasks_agg <- expand_grid(
  outcome = outcomes_list_agg,
  exposure = names(exposure_map_agg)
)

# Run
pwalk(
  list(brms_tasks_agg$outcome, brms_tasks_agg$exposure),
  function(out, exp) {
    run_brms_agg(
      outcome = out, 
      exposure = exp, 
      design_df = ds_ua,
      output_dir = "out/bayesian_models_agg"
    )
    }
  )

## Running aggregate model: bay_dep_har_agg_adj

## Running aggregate model: bay_dep_bul_agg_adj

## Running aggregate model: bay_dep_threats_agg_adj

## Running aggregate model: bay_dep_viol_agg_adj

## Running aggregate model: bay_anx_har_agg_adj

## Running aggregate model: bay_anx_bul_agg_adj

## Running aggregate model: bay_anx_threats_agg_adj

## Running aggregate model: bay_anx_viol_agg_adj

brm_model_names_agg <- ls(pattern = "^bay_", envir = .GlobalEnv)


brm_results_df_agg <- 
  map_dfr(brm_model_names_agg, function(obj_name) {
  
  model <- get(obj_name, envir = .GlobalEnv)
  
  # decode name
  parts <- str_split(obj_name, "_")[[1]]
  
  out_code <- parts[2]
  exp_code <- parts[3]
  prof_code <- parts[4]
  type_code <- parts[5]
  
  # decode elements
  outcome_label <- case_when(
    out_code == "dep"  ~ "Depression",
    out_code == "anx"  ~ "Anxiety"
  )
  
  exposure_label <- case_when(
    exp_code == "har"     ~ "Sexual Harassment",
    exp_code == "bul"     ~ "Bullying",
    exp_code == "threats" ~ "Violent threats",
    exp_code == "viol"    ~ "Physical violence",
    TRUE ~ exp_code
  )
  
  term_lookup <- case_when(
    exp_code == "har"     ~ "work_30_dicYes",
    exp_code == "bul"     ~ "work_33_dicYes",
    exp_code == "threats" ~ "work_31_dicYes",
    exp_code == "viol"    ~ "work_32_dicYes"
  )
  
  
  raw_col_name <- case_when(
    exp_code == "har"     ~ "work_30_dic",
    exp_code == "bul"     ~ "work_33_dic",
    exp_code == "threats" ~ "work_31_dic",
    exp_code == "viol"    ~ "work_32_dic"
  )
  
  
  is_poisson <- model$family$family %in% c("poisson", "binomial")
  
  res <- tidy(
    model,
    effects = "fixed",
    conf.int = TRUE,
    conf.level = 0.95,
    exponentiate = is_poisson
  )
  
  target_row <- 
    res |>
    filter(term == term_lookup)
  
  if(nrow(target_row) > 0) {
    est  <- target_row$estimate[1]
    low  <- target_row$conf.low[1]
    high <- target_row$conf.high[1]
    
    
    
    dat <- model$data
    
    # Check if we can find the columns
    # We look for the raw_col_name we defined above
    if (raw_col_name %in% names(dat)) {
      
      # Extract vectors
      x_vec <- as.numeric(dat[[raw_col_name]])
      # The response is usually the first column in brms data
      y_vec <- as.numeric(dat[[1]]) 
      
      if (is_poisson) {
        # Normalize y to 0/1 if it was imported as 1/2
        if(max(y_vec, na.rm=TRUE) > 1) y_vec <- y_vec - 1
        
        # Calculate Observed Percentages
        # Note: We check for "No" and "Yes" explicitly. 
        # If data is 0/1, change to "0" and "1"
        risk_unexp_val <- mean(y_vec[x_vec == 0], na.rm = TRUE)
        risk_exp_val   <- mean(y_vec[x_vec == 1], na.rm = TRUE)
        
        abs_risk_unexposed <- sprintf("%.1f%%", risk_unexp_val * 100)
        abs_risk_exposed   <- sprintf("%.1f%%", risk_exp_val * 100)
        
      } else {
        # Linear Mean
        mean_unexp_val <- mean(y_vec[x_vec == 0], na.rm = TRUE)
        mean_exp_val   <- mean(y_vec[x_vec == 1], na.rm = TRUE)
        
        abs_risk_unexposed <- sprintf("%.2f", mean_unexp_val)
        abs_risk_exposed   <- sprintf("%.2f", mean_exp_val)
      }
    } else {
      # Debugging: if it still fails, print what columns are actually there
      # warning(paste("Could not find column:", raw_col_name))
      abs_risk_unexposed <- NA
      abs_risk_exposed   <- NA
    }
    
    
    tibble(
      Outcome = outcome_label,
      Exposure = exposure_label,
      Profession = "Overall",
      Est_CI = sprintf("%.2f (%.2f, %.2f)", est, low, high),
      # ADDED: Include the calculated columns here
      Risk_Unexposed = abs_risk_unexposed,
      Risk_Exposed = abs_risk_exposed,
      Rhat = max(rhat(model), na.rm = TRUE)
    )
  } else {
    return(NULL)
  }
})

## Loading required namespace: rstan

brm_results_df_agg

## # A tibble: 8 × 7
##   Outcome    Exposure        Profession Est_CI Risk_Unexposed Risk_Exposed  Rhat
##   <chr>      <chr>           <chr>      <chr>  <chr>          <chr>        <dbl>
## 1 Anxiety    Bullying        Overall    2.34 … NaN%           14.9%         1.00
## 2 Anxiety    Sexual Harassm… Overall    1.87 … NaN%           18.3%         1.00
## 3 Anxiety    Violent threats Overall    2.05 … NaN%           16.8%         1.00
## 4 Anxiety    Physical viole… Overall    1.91 … NaN%           19.4%         1.00
## 5 Depression Bullying        Overall    2.22 … NaN%           21.2%         1.00
## 6 Depression Sexual Harassm… Overall    1.71 … NaN%           26.0%         1.00
## 7 Depression Violent threats Overall    1.94 … NaN%           23.8%         1.00
## 8 Depression Physical viole… Overall    1.72 … NaN%           27.4%         1.00

saveRDS(brm_results_df_agg, "out/dataframes/brm_results_agg.rds")
writexl::write_xlsx(brm_results_df_agg, "out/dataframes/brm_results_agg.xlsx")

ls() |> 
  str_subset("^bay_") |> 
  rm(list = _)

rm(brms_tasks_agg, brm_model_names_agg, run_brms_agg)

Stratified models

# test
# run_brms_pair(outcome = "phq_co", exposure = "work_21_dic", design_df = ds_ua)
# if it works, then

# Define outcome-exposure binomials for analysis
brms_tasks <- expand_grid(
  outcome = outcomes_list,
  exposure = names(exposure_map)
)

# Run
pwalk(
  list(brms_tasks$outcome, brms_tasks$exposure),
  function(out, exp) {
    run_brms_pair(
      outcome = out, 
      exposure = exp, 
      design_df = ds_ua,
      output_dir = "out/bayesian_models"
    )}
  )

## Running doctor model: bay_dep_cols_doc_cr

## Running doctor model: bay_dep_cols_doc_str

## Running doctor model: bay_dep_cols_doc_adj

## Running nurse model (Update): bay_dep_cols_nur_cr

## The desired updates require recompiling the model

## Running nurse model (Update): bay_dep_cols_nur_str

## The desired updates require recompiling the model

## Running nurse model (Update): bay_dep_cols_nur_adj

## The desired updates require recompiling the model

## Running doctor model: bay_dep_sup_doc_cr

## Running doctor model: bay_dep_sup_doc_str

## Running doctor model: bay_dep_sup_doc_adj

## Running nurse model (Update): bay_dep_sup_nur_cr

## The desired updates require recompiling the model

## Running nurse model (Update): bay_dep_sup_nur_str

## The desired updates require recompiling the model

## Running nurse model (Update): bay_dep_sup_nur_adj

## The desired updates require recompiling the model

## Running doctor model: bay_dep_har_doc_cr

## Running doctor model: bay_dep_har_doc_str

## Running doctor model: bay_dep_har_doc_adj

## Running nurse model (Update): bay_dep_har_nur_cr

## The desired updates require recompiling the model

## Running nurse model (Update): bay_dep_har_nur_str

## The desired updates require recompiling the model

## Running nurse model (Update): bay_dep_har_nur_adj

## The desired updates require recompiling the model

## Running doctor model: bay_dep_bul_doc_cr

## Running doctor model: bay_dep_bul_doc_str

## Running doctor model: bay_dep_bul_doc_adj

## Running nurse model (Update): bay_dep_bul_nur_cr

## The desired updates require recompiling the model

## Running nurse model (Update): bay_dep_bul_nur_str

## The desired updates require recompiling the model

## Running nurse model (Update): bay_dep_bul_nur_adj

## The desired updates require recompiling the model

## Running doctor model: bay_dep_threats_doc_cr

## Running doctor model: bay_dep_threats_doc_str

## Running doctor model: bay_dep_threats_doc_adj

## Running nurse model (Update): bay_dep_threats_nur_cr

## The desired updates require recompiling the model

## Running nurse model (Update): bay_dep_threats_nur_str

## The desired updates require recompiling the model

## Running nurse model (Update): bay_dep_threats_nur_adj

## The desired updates require recompiling the model

## Running doctor model: bay_dep_viol_doc_cr

## Running doctor model: bay_dep_viol_doc_str

## Running doctor model: bay_dep_viol_doc_adj

## Running nurse model (Update): bay_dep_viol_nur_cr

## The desired updates require recompiling the model

## Running nurse model (Update): bay_dep_viol_nur_str

## The desired updates require recompiling the model

## Running nurse model (Update): bay_dep_viol_nur_adj

## The desired updates require recompiling the model

## Running doctor model: bay_dep_mean_doc_cr

## Running doctor model: bay_dep_mean_doc_str

## Running doctor model: bay_dep_mean_doc_adj

## Running nurse model (Update): bay_dep_mean_nur_cr

## The desired updates require recompiling the model

## Running nurse model (Update): bay_dep_mean_nur_str

## The desired updates require recompiling the model

## Running nurse model (Update): bay_dep_mean_nur_adj

## The desired updates require recompiling the model

## Running doctor model: bay_dep_purp_doc_cr

## Running doctor model: bay_dep_purp_doc_str

## Running doctor model: bay_dep_purp_doc_adj

## Running nurse model (Update): bay_dep_purp_nur_cr

## The desired updates require recompiling the model

## Running nurse model (Update): bay_dep_purp_nur_str

## The desired updates require recompiling the model

## Running nurse model (Update): bay_dep_purp_nur_adj

## The desired updates require recompiling the model

## Running doctor model: bay_anx_cols_doc_cr

## Running doctor model: bay_anx_cols_doc_str

## Running doctor model: bay_anx_cols_doc_adj

## Running nurse model (Update): bay_anx_cols_nur_cr

## The desired updates require recompiling the model

## Running nurse model (Update): bay_anx_cols_nur_str

## The desired updates require recompiling the model

## Running nurse model (Update): bay_anx_cols_nur_adj

## The desired updates require recompiling the model

## Running doctor model: bay_anx_sup_doc_cr

## Running doctor model: bay_anx_sup_doc_str

## Running doctor model: bay_anx_sup_doc_adj

## Running nurse model (Update): bay_anx_sup_nur_cr

## The desired updates require recompiling the model

## Running nurse model (Update): bay_anx_sup_nur_str

## The desired updates require recompiling the model

## Running nurse model (Update): bay_anx_sup_nur_adj

## The desired updates require recompiling the model

## Running doctor model: bay_anx_har_doc_cr

## Running doctor model: bay_anx_har_doc_str

## Running doctor model: bay_anx_har_doc_adj

## Running nurse model (Update): bay_anx_har_nur_cr

## The desired updates require recompiling the model

## Running nurse model (Update): bay_anx_har_nur_str

## The desired updates require recompiling the model

## Running nurse model (Update): bay_anx_har_nur_adj

## The desired updates require recompiling the model

## Running doctor model: bay_anx_bul_doc_cr

## Running doctor model: bay_anx_bul_doc_str

## Running doctor model: bay_anx_bul_doc_adj

## Running nurse model (Update): bay_anx_bul_nur_cr

## The desired updates require recompiling the model

## Running nurse model (Update): bay_anx_bul_nur_str

## The desired updates require recompiling the model

## Running nurse model (Update): bay_anx_bul_nur_adj

## The desired updates require recompiling the model

## Running doctor model: bay_anx_threats_doc_cr

## Running doctor model: bay_anx_threats_doc_str

## Running doctor model: bay_anx_threats_doc_adj

## Running nurse model (Update): bay_anx_threats_nur_cr

## The desired updates require recompiling the model

## Running nurse model (Update): bay_anx_threats_nur_str

## The desired updates require recompiling the model

## Running nurse model (Update): bay_anx_threats_nur_adj

## The desired updates require recompiling the model

## Running doctor model: bay_anx_viol_doc_cr

## Running doctor model: bay_anx_viol_doc_str

## Running doctor model: bay_anx_viol_doc_adj

## Running nurse model (Update): bay_anx_viol_nur_cr

## The desired updates require recompiling the model

## Running nurse model (Update): bay_anx_viol_nur_str

## The desired updates require recompiling the model

## Running nurse model (Update): bay_anx_viol_nur_adj

## The desired updates require recompiling the model

## Running doctor model: bay_anx_mean_doc_cr

## Running doctor model: bay_anx_mean_doc_str

## Running doctor model: bay_anx_mean_doc_adj

## Running nurse model (Update): bay_anx_mean_nur_cr

## The desired updates require recompiling the model

## Running nurse model (Update): bay_anx_mean_nur_str

## The desired updates require recompiling the model

## Running nurse model (Update): bay_anx_mean_nur_adj

## The desired updates require recompiling the model

## Running doctor model: bay_anx_purp_doc_cr

## Running doctor model: bay_anx_purp_doc_str

## Running doctor model: bay_anx_purp_doc_adj

## Running nurse model (Update): bay_anx_purp_nur_cr

## The desired updates require recompiling the model

## Running nurse model (Update): bay_anx_purp_nur_str

## The desired updates require recompiling the model

## Running nurse model (Update): bay_anx_purp_nur_adj

## The desired updates require recompiling the model

## Running doctor model: bay_suic_cols_doc_cr

## Running doctor model: bay_suic_cols_doc_str

## Running doctor model: bay_suic_cols_doc_adj

## Running nurse model (Update): bay_suic_cols_nur_cr

## The desired updates require recompiling the model

## Running nurse model (Update): bay_suic_cols_nur_str

## The desired updates require recompiling the model

## Running nurse model (Update): bay_suic_cols_nur_adj

## The desired updates require recompiling the model

## Running doctor model: bay_suic_sup_doc_cr

## Running doctor model: bay_suic_sup_doc_str

## Running doctor model: bay_suic_sup_doc_adj

## Running nurse model (Update): bay_suic_sup_nur_cr

## The desired updates require recompiling the model

## Running nurse model (Update): bay_suic_sup_nur_str

## The desired updates require recompiling the model

## Running nurse model (Update): bay_suic_sup_nur_adj

## The desired updates require recompiling the model

## Running doctor model: bay_suic_har_doc_cr

## Running doctor model: bay_suic_har_doc_str

## Running doctor model: bay_suic_har_doc_adj

## Running nurse model (Update): bay_suic_har_nur_cr

## The desired updates require recompiling the model

## Running nurse model (Update): bay_suic_har_nur_str

## The desired updates require recompiling the model

## Running nurse model (Update): bay_suic_har_nur_adj

## The desired updates require recompiling the model

## Running doctor model: bay_suic_bul_doc_cr

## Running doctor model: bay_suic_bul_doc_str

## Running doctor model: bay_suic_bul_doc_adj

## Running nurse model (Update): bay_suic_bul_nur_cr

## The desired updates require recompiling the model

## Running nurse model (Update): bay_suic_bul_nur_str

## The desired updates require recompiling the model

## Running nurse model (Update): bay_suic_bul_nur_adj

## The desired updates require recompiling the model

## Running doctor model: bay_suic_threats_doc_cr

## Running doctor model: bay_suic_threats_doc_str

## Running doctor model: bay_suic_threats_doc_adj

## Running nurse model (Update): bay_suic_threats_nur_cr

## The desired updates require recompiling the model

## Running nurse model (Update): bay_suic_threats_nur_str

## The desired updates require recompiling the model

## Running nurse model (Update): bay_suic_threats_nur_adj

## The desired updates require recompiling the model

## Running doctor model: bay_suic_viol_doc_cr

## Running doctor model: bay_suic_viol_doc_str

## Running doctor model: bay_suic_viol_doc_adj

## Running nurse model (Update): bay_suic_viol_nur_cr

## The desired updates require recompiling the model

## Running nurse model (Update): bay_suic_viol_nur_str

## The desired updates require recompiling the model

## Running nurse model (Update): bay_suic_viol_nur_adj

## The desired updates require recompiling the model

## Running doctor model: bay_suic_mean_doc_cr

## Running doctor model: bay_suic_mean_doc_str

## Running doctor model: bay_suic_mean_doc_adj

## Running nurse model (Update): bay_suic_mean_nur_cr

## The desired updates require recompiling the model

## Running nurse model (Update): bay_suic_mean_nur_str

## The desired updates require recompiling the model

## Running nurse model (Update): bay_suic_mean_nur_adj

## The desired updates require recompiling the model

## Running doctor model: bay_suic_purp_doc_cr

## Running doctor model: bay_suic_purp_doc_str

## Running doctor model: bay_suic_purp_doc_adj

## Running nurse model (Update): bay_suic_purp_nur_cr

## The desired updates require recompiling the model

## Running nurse model (Update): bay_suic_purp_nur_str

## The desired updates require recompiling the model

## Running nurse model (Update): bay_suic_purp_nur_adj

## The desired updates require recompiling the model

Table of results: Bayesian approach

brm_model_names <- ls(pattern = "^bay_", envir = .GlobalEnv)


brm_results_df <- 
  map_dfr(brm_model_names, function(obj_name) {
  
  model <- get(obj_name, envir = .GlobalEnv)
  
  # decode name
  parts <- str_split(obj_name, "_")[[1]]
  
  out_code <- parts[2]
  exp_code <- parts[3]
  prof_code <- parts[4]
  type_code <- parts[5]
  
  # decode elements
  outcome_label <- case_when(
    out_code == "dep"  ~ "Depression",
    out_code == "anx"  ~ "Anxiety",
    out_code == "suic" ~ "Suicide thoughts"
  )
  
  exposure_label <- case_when(
    exp_code == "cols"    ~ "Colleague support",
    exp_code == "sup"     ~ "Support from superiors",
    exp_code == "har"     ~ "Harassment",
    exp_code == "bul"     ~ "Bullying",
    exp_code == "threats" ~ "Violent threats",
    exp_code == "viol"    ~ "Physical violence",
    exp_code == "mean"    ~ "Meaning in work",
    exp_code == "purp"    ~ "Purpose in work",
    TRUE ~ exp_code
  )
  
  profession_label <- ifelse(prof_code == "doc",
                             "Doctor",
                             "Nurse")
  
  
  term_lookup <- case_when(
    exp_code == "cols"    ~ "work_21_dicYes",
    exp_code == "sup"     ~ "work_22_dicYes",
    exp_code == "har"     ~ "work_30_dicYes",
    exp_code == "bul"     ~ "work_33_dicYes",
    exp_code == "threats" ~ "work_31_dicYes",
    exp_code == "viol"    ~ "work_32_dicYes",
    exp_code == "mean"    ~ "wb_wami_1_dicYes",
    exp_code == "purp"    ~ "wb_wami_2_dicYes"
  )
  
  
  raw_col_name <- case_when(
    exp_code == "cols"    ~ "work_21_dic",
    exp_code == "sup"     ~ "work_22_dic",
    exp_code == "har"     ~ "work_30_dic",
    exp_code == "bul"     ~ "work_33_dic",
    exp_code == "threats" ~ "work_31_dic",
    exp_code == "viol"    ~ "work_32_dic",
    exp_code == "mean"    ~ "wb_wami_1_dic",
    exp_code == "purp"    ~ "wb_wami_2_dic"
  )
  
  
  model_label <- case_when(
    type_code == "cr" ~ "Crude",
    type_code == "str" ~ "Partially adjusted",
    type_code == "adj" ~ "Fully adjusted"
  )
  
  is_poisson <- model$family$family %in% c("poisson", "binomial")
  
  res <- tidy(
    model,
    effects = "fixed",
    conf.int = TRUE,
    conf.level = 0.95,
    exponentiate = is_poisson
  )
  
  target_row <- 
    res |>
    filter(term == term_lookup)
  
  if(nrow(target_row) > 0) {
    est  <- target_row$estimate[1]
    low  <- target_row$conf.low[1]
    high <- target_row$conf.high[1]
    
    
    
    dat <- model$data
    
    # Check if we can find the columns
    # We look for the raw_col_name we defined above
    if (raw_col_name %in% names(dat)) {
      
      # Extract vectors
      x_raw <- (dat[[raw_col_name]])
      # The response is usually the first column in brms data
      y_vec <- as.numeric(dat[[1]]) 
      
      if (is.factor(x_raw)) {
         x_vec <- as.numeric(x_raw) - 1  # Converts 1/2 to 0/1
      } else {
         x_vec <- as.numeric(as.character(x_raw)) # "Safe" cast
      }
      
      # Double check
      if (min(x_vec, na.rm = TRUE) == 1 && max(x_vec, na.rm = TRUE) == 2) {
          x_vec <- x_vec - 1
      }
      
      if (is_poisson) {
        # Normalize y to 0/1 if it was imported as 1/2
        if(max(y_vec, na.rm=TRUE) > 1) y_vec <- y_vec - 1
        
        # Calculate Observed Percentages
        # Note: We check for "No" and "Yes" explicitly. 
        # If data is 0/1, change to "0" and "1"
        risk_unexp_val <- mean(y_vec[x_vec == 0], na.rm = TRUE)
        risk_exp_val   <- mean(y_vec[x_vec == 1], na.rm = TRUE)
        
        abs_risk_unexposed <- sprintf("%.1f%%", risk_unexp_val * 100)
        abs_risk_exposed   <- sprintf("%.1f%%", risk_exp_val * 100)
        
      } else {
        # Linear Mean
        mean_unexp_val <- mean(y_vec[x_vec == 0], na.rm = TRUE)
        mean_exp_val   <- mean(y_vec[x_vec == 1], na.rm = TRUE)
        
        abs_risk_unexposed <- sprintf("%.2f", mean_unexp_val)
        abs_risk_exposed   <- sprintf("%.2f", mean_exp_val)
      }
    } else {
      # Debugging: if it still fails, print what columns are actually there
      # warning(paste("Could not find column:", raw_col_name))
      abs_risk_unexposed <- NA
      abs_risk_exposed   <- NA
    }
    
    
    tibble(
      Outcome = outcome_label,
      Exposure = exposure_label,
      Profession = profession_label,
      Model = model_label,
      Type = ifelse(is_poisson, "PR", "Beta"),
      Est_CI = sprintf("%.2f (%.2f, %.2f)", est, low, high),
      # ADDED: Include the calculated columns here
      Risk_Unexposed = abs_risk_unexposed,
      Risk_Exposed = abs_risk_exposed,
      Rhat = max(rhat(model), na.rm = TRUE)
    )
  } else {
    
    return(NULL)
  
    }
  })

brm_results_df

## # A tibble: 144 × 9
##    Outcome Exposure    Profession Model Type  Est_CI Risk_Unexposed Risk_Exposed
##    <chr>   <chr>       <chr>      <chr> <chr> <chr>  <chr>          <chr>       
##  1 Anxiety Bullying    Doctor     Full… PR    2.09 … 18.3%          42.3%       
##  2 Anxiety Bullying    Doctor     Crude PR    2.31 … 18.2%          42.4%       
##  3 Anxiety Bullying    Doctor     Part… PR    2.08 … 18.2%          42.3%       
##  4 Anxiety Bullying    Nurse      Full… PR    2.64 … 12.5%          35.1%       
##  5 Anxiety Bullying    Nurse      Crude PR    2.74 … 12.5%          34.9%       
##  6 Anxiety Bullying    Nurse      Part… PR    2.63 … 12.4%          34.9%       
##  7 Anxiety Colleague … Doctor     Full… PR    0.65 … 32.0%          19.9%       
##  8 Anxiety Colleague … Doctor     Crude PR    0.63 … 32.0%          19.9%       
##  9 Anxiety Colleague … Doctor     Part… PR    0.65 … 31.9%          19.9%       
## 10 Anxiety Colleague … Nurse      Full… PR    0.58 … 22.2%          12.6%       
## # ℹ 134 more rows
## # ℹ 1 more variable: Rhat <dbl>

# Save
saveRDS(brm_results_df, "out/dataframes/brm_results.rds")
writexl::write_xlsx(brm_results_df, "out/dataframes/brm_results.xlsx")

brm_results_df <- 
  readRDS("out/dataframes/brm_results.rds") |> 
  mutate(Exposure = ifelse(Exposure == "Harassment",
                           "Sexual Harassment", # Naming check
                           Exposure))

# Build table with function
tbl_bay_res <- 
  build_bay_gt(brm_results_df, "PR", 
               "**Bayesian Models**", 
               "Posterior Median PR (95% CrI). Models specified with weakly informative priors: These represent prior beliefs of the estimates. Using them gives more flexibility for the data. All models include Region as a second order covariate.")


tbl_bay_res

Bayesian Models
	Doctor			Nurse
	Crude1	Partially adjusted2	Fully adjusted3	Crude1	Partially adjusted2	Fully adjusted3
Depression
Bullying	2.21 (2.06, 2.37)	2.01 (1.87, 2.17)	2.02 (1.87, 2.17)	2.63 (2.42, 2.86)	2.48 (2.29, 2.69)	2.48 (2.28, 2.69)
Sexual Harassment	1.77 (1.61, 1.95)	1.48 (1.34, 1.63)	1.48 (1.34, 1.65)	2.35 (2.11, 2.61)	2.06 (1.83, 2.30)	2.06 (1.84, 2.31)
Violent threats	1.99 (1.83, 2.15)	1.83 (1.70, 1.98)	1.84 (1.70, 1.99)	2.26 (2.05, 2.48)	2.10 (1.90, 2.32)	2.11 (1.91, 2.32)
Physical violence	1.66 (1.41, 1.93)	1.47 (1.24, 1.72)	1.48 (1.25, 1.73)	2.19 (1.88, 2.53)	1.99 (1.70, 2.32)	1.99 (1.69, 2.33)
Colleague support	0.63 (0.59, 0.68)	0.66 (0.62, 0.71)	0.66 (0.61, 0.71)	0.58 (0.54, 0.63)	0.60 (0.55, 0.65)	0.60 (0.55, 0.65)
Support from superiors	0.58 (0.54, 0.63)	0.62 (0.57, 0.67)	0.62 (0.57, 0.67)	0.50 (0.46, 0.54)	0.52 (0.48, 0.56)	0.52 (0.48, 0.56)
Meaning in work	0.48 (0.44, 0.53)	0.50 (0.46, 0.55)	0.50 (0.46, 0.55)	0.42 (0.38, 0.46)	0.44 (0.40, 0.48)	0.44 (0.40, 0.48)
Purpose in work	0.46 (0.41, 0.50)	0.48 (0.44, 0.53)	0.48 (0.44, 0.54)	0.46 (0.42, 0.52)	0.49 (0.44, 0.54)	0.49 (0.44, 0.55)
Anxiety
Bullying	2.31 (2.12, 2.51)	2.08 (1.91, 2.27)	2.09 (1.91, 2.28)	2.74 (2.50, 3.01)	2.63 (2.39, 2.89)	2.64 (2.40, 2.92)
Sexual Harassment	1.95 (1.74, 2.18)	1.60 (1.42, 1.80)	1.61 (1.42, 1.81)	2.49 (2.20, 2.80)	2.24 (1.96, 2.56)	2.25 (1.97, 2.57)
Violent threats	2.05 (1.87, 2.24)	1.90 (1.74, 2.09)	1.92 (1.75, 2.11)	2.32 (2.08, 2.57)	2.22 (1.98, 2.49)	2.25 (1.99, 2.52)
Physical violence	1.91 (1.58, 2.27)	1.70 (1.41, 2.03)	1.75 (1.46, 2.09)	2.18 (1.83, 2.57)	2.06 (1.72, 2.46)	2.08 (1.75, 2.46)
Colleague support	0.63 (0.57, 0.68)	0.65 (0.60, 0.71)	0.65 (0.60, 0.71)	0.57 (0.52, 0.63)	0.58 (0.53, 0.64)	0.58 (0.53, 0.63)
Support from superiors	0.57 (0.52, 0.62)	0.60 (0.55, 0.65)	0.60 (0.54, 0.65)	0.50 (0.45, 0.55)	0.52 (0.47, 0.57)	0.52 (0.47, 0.57)
Meaning in work	0.48 (0.43, 0.53)	0.50 (0.45, 0.56)	0.50 (0.45, 0.56)	0.46 (0.41, 0.52)	0.47 (0.42, 0.53)	0.47 (0.42, 0.53)
Purpose in work	0.44 (0.39, 0.50)	0.47 (0.42, 0.53)	0.47 (0.42, 0.53)	0.49 (0.44, 0.56)	0.51 (0.45, 0.58)	0.51 (0.45, 0.59)
Suicide thoughts
Bullying	2.45 (2.18, 2.76)	2.20 (1.95, 2.47)	2.20 (1.94, 2.48)	3.30 (2.90, 3.78)	3.08 (2.70, 3.53)	3.05 (2.69, 3.46)
Sexual Harassment	2.55 (2.20, 2.94)	2.11 (1.82, 2.45)	2.10 (1.81, 2.45)	3.21 (2.71, 3.77)	2.75 (2.33, 3.26)	2.72 (2.30, 3.21)
Violent threats	2.45 (2.16, 2.76)	2.15 (1.90, 2.44)	2.16 (1.90, 2.44)	2.80 (2.42, 3.21)	2.54 (2.19, 2.95)	2.52 (2.16, 2.92)
Physical violence	2.71 (2.19, 3.31)	2.25 (1.82, 2.77)	2.26 (1.81, 2.79)	2.94 (2.35, 3.67)	2.57 (2.06, 3.20)	2.56 (2.05, 3.19)
Colleague support	0.61 (0.54, 0.68)	0.64 (0.57, 0.72)	0.64 (0.57, 0.72)	0.52 (0.45, 0.59)	0.53 (0.46, 0.61)	0.53 (0.46, 0.60)
Support from superiors	0.57 (0.50, 0.64)	0.61 (0.54, 0.69)	0.61 (0.54, 0.69)	0.54 (0.47, 0.62)	0.56 (0.49, 0.64)	0.56 (0.49, 0.63)
Meaning in work	0.41 (0.36, 0.47)	0.44 (0.38, 0.51)	0.45 (0.38, 0.52)	0.46 (0.39, 0.53)	0.48 (0.41, 0.56)	0.48 (0.41, 0.56)
Purpose in work	0.36 (0.31, 0.42)	0.39 (0.34, 0.47)	0.40 (0.34, 0.47)	0.43 (0.36, 0.51)	0.45 (0.38, 0.54)	0.45 (0.38, 0.54)
Posterior Median PR (95% CrI). Models specified with weakly informative priors: These represent prior beliefs of the estimates. Using them gives more flexibility for the data. All models include Region as a second order covariate.
1 Region as second-order covariate only.
2 Adjusted by Gender, Age group and Proximity to combat areas and occupied territories .
3 Partially adjusted models plus specific covariates: Bullying and sexual harassment adjusted by Healthcare setting, Post-graduate training and Length of service. Violent threats and physical violence adjusted by Healthcare setting. Support from colleagues or superiors adjusted by Healthcare setting and Length of service. Finding meaning or purpose in job adjusted by Healthcare setting, Relationship status, Having children, and Length of service.

gtsave(tbl_bay_res, "out/tables/bay_res_tbl.docx")


ls() |> 
  str_subset("^bay_") |> 
  rm(list = _)

rm(brms_tasks, bin_priors, brm_model_names, run_brms_pair, build_bay_gt)

Sensitivity analyses: Restrictive priors

Restrictive priors

# for sensitivity analyses, there is a previous article with GAD-7 and PHQ-9 
# prevalences on physicians: https://doi.org/10.1016/j.psychres.2024.115836

phq_doc_p <- 0.108
intercept_prior_phq <- log(phq_doc_p)

priors_res_phq_doc <- c(
  # informative intercept based on the article, small SD (0.5)
  prior_string(glue("normal({intercept_prior_phq}, 0.5)"), 
               class = "Intercept"),
  
  # restrictive predictors (no PR known)
  # force conservative model, with sd of 0.25, forces data to prove effect
  prior(normal(0, 0.25), class = b),
  
  # weak random effect (standard practice)
  prior(student_t(3, 0, 1), class = sd)
)

gad_doc_p <- 0.165
intercept_prior_gad <- log(gad_doc_p)

priors_res_gad_doc <- c(
  # informative intercept based on the article
  prior_string(glue("normal({intercept_prior_gad}, 0.5)"), 
               class = "Intercept"),
  
  # restrictive predictors (no PR known)
  # force model to be conservative
  prior(normal(0, 0.25), class = b),
  
  # weak random effect (standard practice)
  prior(student_t(3, 0, 1), class = sd)
)
  

# for nursing we do not have information, sorestrict with skeptical betas
# (Vague but mathematically valid for Log-Link)
# Centered on ~13% prevalence, but wide enough to cover 0% to ~50%
priors_sens_nur <- c(
  prior(normal(-2, 1.5), class = Intercept),
  
  # same skepticism to exposure effects
  prior(normal(0, 0.25), class = b),
  
  prior(student_t(3, 0, 1), class = sd)
) 

Models with restrictive priors

# define only phq and gad, is the information we have available
sens_tasks <- expand_grid(
  outcome = c("phq_co", "gad_co"), # restrict here
  exposure = names(exposure_map)
)

# Run
pwalk(
  list(sens_tasks$outcome, sens_tasks$exposure),
  function(out, exp) {
    
    message(glue("\nRunning Sensitivity Analysis for: {out} + {exp}"))
    run_brms_sensitivity(
      outcome = out, 
      exposure = exp, 
      design_df = ds_ua,
      output_dir = "out/bayesian_models_sens" # Saves in new folder
    )
  }
)

## Running Sensitivity Analysis for: phq_co + work_21_dic

## >> Doc Prior: PHQ Informative (Log-Odds: -2.23)

## >> Nur Prior: Weak Intercept + Skeptical Beta

## Running Sensitivity Analysis for: phq_co + work_22_dic

## >> Doc Prior: PHQ Informative (Log-Odds: -2.23)

## >> Nur Prior: Weak Intercept + Skeptical Beta

## Running Sensitivity Analysis for: phq_co + work_30_dic

## >> Doc Prior: PHQ Informative (Log-Odds: -2.23)

## >> Nur Prior: Weak Intercept + Skeptical Beta

## Running Sensitivity Analysis for: phq_co + work_33_dic

## >> Doc Prior: PHQ Informative (Log-Odds: -2.23)

## >> Nur Prior: Weak Intercept + Skeptical Beta

## Running Sensitivity Analysis for: phq_co + work_31_dic

## >> Doc Prior: PHQ Informative (Log-Odds: -2.23)

## >> Nur Prior: Weak Intercept + Skeptical Beta

## Running Sensitivity Analysis for: phq_co + work_32_dic

## >> Doc Prior: PHQ Informative (Log-Odds: -2.23)

## >> Nur Prior: Weak Intercept + Skeptical Beta

## Running Sensitivity Analysis for: phq_co + wb_wami_1_dic

## >> Doc Prior: PHQ Informative (Log-Odds: -2.23)

## >> Nur Prior: Weak Intercept + Skeptical Beta

## Running Sensitivity Analysis for: phq_co + wb_wami_2_dic

## >> Doc Prior: PHQ Informative (Log-Odds: -2.23)

## >> Nur Prior: Weak Intercept + Skeptical Beta

## Running Sensitivity Analysis for: gad_co + work_21_dic

## >> Doc Prior: GAD Informative (Log-Odds: -1.8)

## >> Nur Prior: Weak Intercept + Skeptical Beta

## Running Sensitivity Analysis for: gad_co + work_22_dic

## >> Doc Prior: GAD Informative (Log-Odds: -1.8)

## >> Nur Prior: Weak Intercept + Skeptical Beta

## Running Sensitivity Analysis for: gad_co + work_30_dic

## >> Doc Prior: GAD Informative (Log-Odds: -1.8)

## >> Nur Prior: Weak Intercept + Skeptical Beta

## Running Sensitivity Analysis for: gad_co + work_33_dic

## >> Doc Prior: GAD Informative (Log-Odds: -1.8)

## >> Nur Prior: Weak Intercept + Skeptical Beta

## Running Sensitivity Analysis for: gad_co + work_31_dic

## >> Doc Prior: GAD Informative (Log-Odds: -1.8)

## >> Nur Prior: Weak Intercept + Skeptical Beta

## Running Sensitivity Analysis for: gad_co + work_32_dic

## >> Doc Prior: GAD Informative (Log-Odds: -1.8)

## >> Nur Prior: Weak Intercept + Skeptical Beta

## Running Sensitivity Analysis for: gad_co + wb_wami_1_dic

## >> Doc Prior: GAD Informative (Log-Odds: -1.8)

## >> Nur Prior: Weak Intercept + Skeptical Beta

## Running Sensitivity Analysis for: gad_co + wb_wami_2_dic

## >> Doc Prior: GAD Informative (Log-Odds: -1.8)

## >> Nur Prior: Weak Intercept + Skeptical Beta

# get files
sens_files <- list.files("out/bayesian_models_sens", 
                         full.names = TRUE)

# extract data
bay_sens_res_df <- map_dfr(sens_files, 
                           function(file_path) {
  
  # parse filename to get metadata
  fname <- str_remove(basename(file_path), ".rds")
  parts <- str_split(fname, "_")[[1]]
  
  # parts[1] = "bay", parts[2] = "sens"
  out_code  <- parts[3]
  exp_code  <- parts[4]
  prof_code <- parts[5]
  type_code <- parts[6] # cr, str, or adj
  
  # We only care about Fully Adjusted for the table
  if (type_code != "adj") return(NULL)
  
  # Decode Labels (Same as before)
  outcome_label <- case_when(
    out_code == "dep" ~ "Depression",
    out_code == "anx" ~ "Anxiety"
  )
  
  exposure_label <- case_when(
    exp_code == "cols"    ~ "Colleague support",
    exp_code == "sup"     ~ "Support from superiors",
    exp_code == "har"     ~ "Sexual Harassment",
    exp_code == "bul"     ~ "Bullying",
    exp_code == "threats" ~ "Violent threats",
    exp_code == "viol"    ~ "Physical violence",
    exp_code == "mean"    ~ "Meaning in work",
    exp_code == "purp"    ~ "Purpose in work"
  )
  
  profession_label <- ifelse(prof_code == "doc", "Doctor", "Nurse")
  
  # Determine Exposure Term
  term_lookup <- case_when(
    exp_code == "cols"    ~ "work_21_dicYes",
    exp_code == "sup"     ~ "work_22_dicYes",
    exp_code == "har"     ~ "work_30_dicYes",
    exp_code == "bul"     ~ "work_33_dicYes",
    exp_code == "threats" ~ "work_31_dicYes",
    exp_code == "viol"    ~ "work_32_dicYes",
    exp_code == "mean"    ~ "wb_wami_1_dicYes",
    exp_code == "purp"    ~ "wb_wami_2_dicYes"
  )
  
  # Load Model
  model <- readRDS(file_path)
  
  # Tidy
  res <- tidy(
    model, 
    effects = "fixed", 
    conf.int = TRUE, 
    conf.level = 0.95, 
    exponentiate = TRUE
    )
  
  target <- res |> filter(term == term_lookup)
  
  if(nrow(target) > 0) {
    est <- target$estimate[1]
    low <- target$conf.low[1]
    high <- target$conf.high[1]
    
    tibble(
      Outcome = outcome_label,
      Exposure = exposure_label,
      Profession = profession_label,
      Analysis = "Sensitivity (Restrictive)", # Mark this row
      Est_CI = sprintf("%.2f (%.2f, %.2f)", est, low, high)
    )
  } else {
    NULL
  }
})

bay_sens_res_df

## # A tibble: 32 × 5
##    Outcome Exposure          Profession Analysis                  Est_CI        
##    <chr>   <chr>             <chr>      <chr>                     <chr>         
##  1 Anxiety Bullying          Doctor     Sensitivity (Restrictive) 2.06 (1.89, 2…
##  2 Anxiety Bullying          Nurse      Sensitivity (Restrictive) 2.58 (2.34, 2…
##  3 Anxiety Colleague support Doctor     Sensitivity (Restrictive) 0.66 (0.61, 0…
##  4 Anxiety Colleague support Nurse      Sensitivity (Restrictive) 0.58 (0.53, 0…
##  5 Anxiety Sexual Harassment Doctor     Sensitivity (Restrictive) 1.59 (1.42, 1…
##  6 Anxiety Sexual Harassment Nurse      Sensitivity (Restrictive) 2.18 (1.91, 2…
##  7 Anxiety Meaning in work   Doctor     Sensitivity (Restrictive) 0.51 (0.46, 0…
##  8 Anxiety Meaning in work   Nurse      Sensitivity (Restrictive) 0.49 (0.44, 0…
##  9 Anxiety Purpose in work   Doctor     Sensitivity (Restrictive) 0.49 (0.43, 0…
## 10 Anxiety Purpose in work   Nurse      Sensitivity (Restrictive) 0.53 (0.47, 0…
## # ℹ 22 more rows

# Save
writexl::write_xlsx(bay_sens_res_df, "out/dataframes/bay_restr_res.xlsx")
saveRDS(bay_sens_res_df, "out/dataframes/bay_res_sens.rds")

bay_results_df <- 
  readRDS("out/dataframes/brm_results.rds") |> 
  mutate(Exposure = case_when(
    Exposure == "Harassment" ~ "Sexual Harassment",
    TRUE ~ Exposure
  ))

# Prepare results, filter full models
main_subset <- 
  
  bay_results_df |>
  filter(Model == "Fully adjusted") |>
  # outcomes studied for sensitivity
  filter(Outcome %in% c("Depression", "Anxiety")) |>
  select(Outcome, Exposure, Profession, Est_CI) |>
  mutate(Analysis = "Main (Weak Priors)")


# load sensitivity df
bay_res_sens_df <- readRDS("out/dataframes/bay_res_sens.rds")


# prepare ds
sens_subset <- 
  
  bay_res_sens_df |>
  select(Outcome, Exposure, Profession, Est_CI, Analysis)

# Merge
bay_comparison_df <- 
  
  bind_rows(main_subset, sens_subset) |> 
  mutate(
    # Extract numbers
    PR = as.numeric(str_extract(Est_CI, "^[0-9\\.]+")),
    Lower = as.numeric(str_extract(Est_CI, "(?<=\\()[0-9\\.]+")),
    Upper = as.numeric(str_extract(Est_CI, "[0-9\\.]+(?=\\))")),
    
    # FIX ORDER: Convert to factor. 
    # We use rev() so "Bullying" appears at the TOP of the plot
    
    Exposure = factor(Exposure, levels = rev(exposure_order))
  )


# table

tbl_sensitivity <- 
  
  bay_comparison_df |>
  select(Outcome, Exposure, Profession, Analysis, Est_CI) |> 
  # Pivot to wide format
  pivot_wider(
    names_from = c(Profession, Analysis),
    values_from = Est_CI,
    names_sep = "_"
  ) |>
  # Order columns logically
  select(Outcome, Exposure, 
         `Doctor_Main (Weak Priors)`, `Doctor_Sensitivity (Restrictive)`,
         `Nurse_Main (Weak Priors)`, `Nurse_Sensitivity (Restrictive)`) |>
  mutate(Outcome = factor(Outcome, levels = c("Depression", "Anxiety")),
         Exposure = factor(Exposure, levels = exposure_order) 
         )|>
  arrange(Outcome, Exposure) |> 
  # Create Table
  gt(
    groupname_col = "Outcome", 
    rowname_col = "Exposure"
    ) |>
  
  # Headers
  tab_header(
    title = md("**Sensitivity Analysis: Restrictive Priors**"),
    subtitle = "Fully Adjusted Prevalence Ratios (95% CrI)"
  ) |>
  
  # Spanners for Profession
  tab_spanner(
    label = md("**Doctor**"),
    columns = starts_with("Doctor")
  ) |>
  tab_spanner(
    label = md("**Nurse**"),
    columns = starts_with("Nurse")
  ) |>
  
  # Clean Column Labels (Remove the "Doctor_" prefix)
  cols_label(
    `Doctor_Main (Weak Priors)` = "Weakly Informative Priors",
    `Doctor_Sensitivity (Restrictive)` = "Restrictive Priors",
    `Nurse_Main (Weak Priors)` = "Weakly Informative Priors",
    `Nurse_Sensitivity (Restrictive)` = "Restrictive Priors"
  ) |>
  
  # Formatting
  cols_align(align = "center", columns = contains("_")) |>
  tab_stub_indent(rows = everything(), indent = 3) |>
  tab_footnote(
    footnote = paste0( "Main Analysis: Weak priors (Normal(-1.5,1)). Restrictive: Doctors use informative intercepts based on prevalence + Skeptical Betas (Normal(0, 0.25)). Nurses use Skeptical Betas. All models include Region as a second-order covariate. Adjusted by Age group, Gender and Proximity to combat areas and occupied territories plus specific covariates:", "\n",
                 "Bullying and sexual harassment adjusted by Healthcare setting, Post-graduate training and Length of service.", "\n",
                 "Violent threats and physical violence adjusted by Healthcare setting.", "\n",
                 "Support from colleagues or superiors adjusted by Healthcare setting and Length of service.", "\n",
                 "Finding meaning or purpose in job adjusted by Healthcare setting, Relationship status, Having children, and Length of service."
  ))

# Save
gtsave(tbl_sensitivity, "out/tables/bayes_sensitivity_comparison.docx")

tbl_sensitivity

Sensitivity Analysis: Restrictive Priors
Fully Adjusted Prevalence Ratios (95% CrI)
	Doctor		Nurse
	Weakly Informative Priors	Restrictive Priors	Weakly Informative Priors	Restrictive Priors
Depression
Bullying	2.02 (1.87, 2.17)	2.00 (1.86, 2.15)	2.48 (2.28, 2.69)	2.44 (2.26, 2.65)
Sexual Harassment	1.48 (1.34, 1.65)	1.47 (1.33, 1.63)	2.06 (1.84, 2.31)	2.03 (1.81, 2.27)
Violent threats	1.84 (1.70, 1.99)	1.83 (1.69, 1.97)	2.11 (1.91, 2.32)	2.07 (1.88, 2.29)
Physical violence	1.48 (1.25, 1.73)	1.44 (1.23, 1.67)	1.99 (1.69, 2.33)	1.91 (1.65, 2.21)
Colleague support	0.66 (0.61, 0.71)	0.66 (0.62, 0.71)	0.60 (0.55, 0.65)	0.60 (0.56, 0.65)
Support from superiors	0.62 (0.57, 0.67)	0.62 (0.58, 0.67)	0.52 (0.48, 0.56)	0.52 (0.48, 0.57)
Meaning in work	0.50 (0.46, 0.55)	0.51 (0.47, 0.56)	0.44 (0.40, 0.48)	0.45 (0.41, 0.49)
Purpose in work	0.48 (0.44, 0.54)	0.49 (0.45, 0.55)	0.49 (0.44, 0.55)	0.50 (0.45, 0.56)
Anxiety
Bullying	2.09 (1.91, 2.28)	2.06 (1.89, 2.25)	2.64 (2.40, 2.92)	2.58 (2.34, 2.84)
Sexual Harassment	1.61 (1.42, 1.81)	1.59 (1.42, 1.78)	2.25 (1.97, 2.57)	2.18 (1.91, 2.48)
Violent threats	1.92 (1.75, 2.11)	1.89 (1.72, 2.08)	2.25 (1.99, 2.52)	2.19 (1.96, 2.43)
Physical violence	1.75 (1.46, 2.09)	1.67 (1.39, 1.98)	2.08 (1.75, 2.46)	1.96 (1.65, 2.32)
Colleague support	0.65 (0.60, 0.71)	0.66 (0.61, 0.72)	0.58 (0.53, 0.63)	0.58 (0.53, 0.64)
Support from superiors	0.60 (0.54, 0.65)	0.60 (0.55, 0.65)	0.52 (0.47, 0.57)	0.52 (0.48, 0.57)
Meaning in work	0.50 (0.45, 0.56)	0.51 (0.46, 0.57)	0.47 (0.42, 0.53)	0.49 (0.44, 0.54)
Purpose in work	0.47 (0.42, 0.53)	0.49 (0.43, 0.55)	0.51 (0.45, 0.59)	0.53 (0.47, 0.60)
Main Analysis: Weak priors (Normal(-1.5,1)). Restrictive: Doctors use informative intercepts based on prevalence + Skeptical Betas (Normal(0, 0.25)). Nurses use Skeptical Betas. All models include Region as a second-order covariate. Adjusted by Age group, Gender and Proximity to combat areas and occupied territories plus specific covariates: Bullying and sexual harassment adjusted by Healthcare setting, Post-graduate training and Length of service. Violent threats and physical violence adjusted by Healthcare setting. Support from colleagues or superiors adjusted by Healthcare setting and Length of service. Finding meaning or purpose in job adjusted by Healthcare setting, Relationship status, Having children, and Length of service.

#plot

# Create a clean plot
forest_bayes_comp <- 
  
  bay_comparison_df |>
  # extract numeric estimates for plotting
  mutate(
    PR = as.numeric(str_extract(Est_CI, "^[0-9\\.]+")),
    Lower = as.numeric(str_extract(Est_CI, "(?<=\\()[0-9\\.]+")),
    Upper = as.numeric(str_extract(Est_CI, "[0-9\\.]+(?=\\))"))
  ) |>
  mutate(Outcome = factor(Outcome, levels = c("Depression", "Anxiety"))) |> 
  ggplot(aes(x = PR, y = Exposure, shape = Analysis)) +
  geom_point(position = position_dodge(width = 0.5), size = 3) +
  geom_errorbar(aes(xmin = Lower, xmax = Upper), 
                 position = position_dodge(width = 0.5)) +
  facet_wrap(~Profession + Outcome, scales = "free_y") +
  geom_vline(xintercept = 1, linetype = "dashed") +
  scale_x_log10(breaks = c(0.2, 0.5, 1, 2, 4),
                labels = scales::label_number(
                  accuracy = 0.1,
                  decimal.mark = "·"
                )) +
  ggpubr::theme_pubr() + 
  theme(
    legend.text = element_text(size = 10),
    axis.text.x = element_text(size = 10),
    axis.title.x = element_text(size = 10),
    axis.text.y = element_text(size = 10),
    axis.title.y = element_text(size = 10),
    strip.text = element_text(size = 10)
  )

forest_bayes_comp

ggsave(filename = "forest_bayes_comparison.pdf",
       plot = forest_bayes_comp,
       path = "out/plots/",
       width = 210,
       height = 170,
       units = "mm",
       dpi = 320,
       scale = 1.5
       )

Model diagnostics

# define file paths for each 
main_files <- list.files("out/bayesian_models",
                         full.names = TRUE,
                         pattern = "\\.rds$")

agg_files <- list.files("out/bayesian_models_agg",
                         full.names = TRUE,
                         pattern = "\\.rds$")

sens_files <- list.files("out/bayesian_models_sens",
                         full.names = TRUE,
                         pattern = "\\.rds$")


# execute
df_main_diag <- process_disk_models(main_files, 
                                    "Weakly Informative Priors")
df_agg_diag <- process_disk_models(agg_files, "Weakly Informative Priors")
df_sens_diag <- process_disk_models(sens_files, "Restrictive Priors")

# Combine and clean
final_diagnostics <- 
  
  bind_rows(df_main_diag, df_agg_diag, df_sens_diag) |> 
  mutate(clean_id = str_remove(model_id, "bay_")) |> 
  mutate(clean_id = str_remove(clean_id, "sens_")) |> 
  separate(
    clean_id,
    into = c("outcome", "exposure", "group", "adjustment"),
    sep = "_",
    extra = "merge"
  ) |> 
  mutate(
    outcome = case_match(outcome,
                         "dep" ~ "Depression",
                         "anx" ~ "Anxiety",
                         "suic" ~ "Suicide thoughts",
                         .default = outcome
                         ),
    exposure = case_match(exposure,
                          "bul" ~ "Bullying",
                          "har" ~ "Sexual Harassment",
                          "threats" ~ "Violent threats",
                          "viol" ~ "Physical violence",
                          "cols" ~ "Colleague support",
                          "sup" ~ "Support from superiors",
                          "mean" ~ "Meaning in work",
                          "purp" ~ "Purpose in work",
                          .default = exposure),
    group = case_match(group,
                       "agg" ~ "Aggregate",
                       "doc" ~ "Doctor",
                       "nur" ~ "Nurse",
                       .default = group),
    adjustment = case_match(adjustment,
                            "cr" ~ "Crude",
                            "str" ~ "Partially adjusted",
                            "adj" ~ "Fully adjusted",
                            .default = adjustment)
    ) |> 
  filter(outcome != "wb") |> 
  select(Type, outcome, exposure, group, adjustment, max_rhat, min_ess_bulk, 
         divergences)


final_diagnostics

## # A tibble: 248 × 8
##    Type      outcome exposure group adjustment max_rhat min_ess_bulk divergences
##    <chr>     <chr>   <chr>    <chr> <chr>         <dbl>        <dbl>       <int>
##  1 Weakly I… Anxiety Bullying Doct… Fully adj…     1.00        1112.           0
##  2 Weakly I… Anxiety Bullying Doct… Crude          1.00        1130.           0
##  3 Weakly I… Anxiety Bullying Doct… Partially…     1.00        1010.           0
##  4 Weakly I… Anxiety Bullying Nurse Fully adj…     1.00         800.           0
##  5 Weakly I… Anxiety Bullying Nurse Crude          1.00         901.           0
##  6 Weakly I… Anxiety Bullying Nurse Partially…     1.00         827.           0
##  7 Weakly I… Anxiety Colleag… Doct… Fully adj…     1.00        1009.           0
##  8 Weakly I… Anxiety Colleag… Doct… Crude          1.01        1060.           0
##  9 Weakly I… Anxiety Colleag… Doct… Partially…     1.01        1083.           0
## 10 Weakly I… Anxiety Colleag… Nurse Fully adj…     1.00        1060.           0
## # ℹ 238 more rows

# Save
saveRDS(final_diagnostics, "out/dataframes/bayes_diagnostics.rds")
writexl::write_xlsx(final_diagnostics, 
                    "out/dataframes/bayes_diagnostics.xlsx")

# load 4 models, 2 main and 2 sensitivity, same out x exp, both prof
bay_dep_bul_doc_adj <- 
  readRDS("out/bayes_diagnostic_mods/bay_dep_bul_doc_adj.rds")

bay_dep_bul_nur_adj <- 
  readRDS("out/bayes_diagnostic_mods/bay_dep_bul_nur_adj.rds")

bay_sens_dep_bul_doc_adj <- 
  readRDS("out/bayes_diagnostic_mods/bay_sens_dep_bul_doc_adj.rds")

bay_sens_dep_bul_nur_adj <- 
  readRDS("out/bayes_diagnostic_mods/bay_sens_dep_bul_nur_adj.rds")


# run pipeline
plots_main_doc <- 
  create_diag_plots(bay_dep_bul_doc_adj, 
                    "Doctors (Weakly Informative)",
                    "b_work_33_dicYes")

## No divergences to plot.

plots_main_nur <- 
  create_diag_plots(bay_dep_bul_nur_adj, 
                    "Nurses (Weakly Informative)", 
                    "b_work_33_dicYes")

## No divergences to plot.

plots_sens_doc <- 
  create_diag_plots(bay_sens_dep_bul_doc_adj, 
                    "Doctors (Restrictive)", 
                    "b_work_33_dicYes")

## No divergences to plot.

plots_sens_nur <- 
  create_diag_plots(bay_sens_dep_bul_nur_adj, 
                    "Nurses (Restrictive)",
                    "b_work_33_dicYes")

## No divergences to plot.

# grid
final_dx_plots_bayes <- (
  (plots_main_doc$trace + plots_main_doc$ppc) /
  (plots_main_nur$trace + plots_main_nur$ppc) /
  (plots_sens_doc$trace + plots_sens_doc$ppc) /
  (plots_sens_nur$trace + plots_sens_nur$ppc)
  ) + 
  plot_layout(heights = c(1, 1, 1, 1))

# View
final_dx_plots_bayes

ggsave(
  "bayes_diagnostic_plots.pdf",
  plot = final_dx_plots_bayes,
  path = "out/plots/",
  scale = 1.5,
  width = 220,
  height = 160,
  unit = "mm",
  dpi = 350
)

rm(bay_dep_bul_doc_adj, bay_dep_bul_nur_adj, bay_sens_dep_bul_doc_adj,
   bay_sens_dep_bul_nur_adj, final_dx_plots_bayes)

bayes_dx <- 
  readRDS("out/dataframes/bayes_diagnostics.rds") |> 
  mutate(exposure = if_else(exposure == "purp",
                           "Purpose in work",
                           exposure))

# edit datasets
data_main <- prepare_dx_table_data(bayes_dx, "Weakly Informative Priors")
data_sens <- prepare_dx_table_data(bayes_dx, "Restrictive Priors")


# create table

tbl_main_dx <-
  
  data_main |>
  mutate(outcome = factor(outcome, levels = outcome_order),
         adjustment = factor(adjustment, levels = adjustment_order),
         exposure = factor(exposure, levels = exposure_order)
         ) |>
  arrange(outcome, exposure, adjustment) |>
  # doing this the output in word will look ok
  group_by(outcome, exposure) |> 
  mutate(
    exposure_label = ifelse(row_number() == 1, as.character(exposure), "") 
  ) |> 
  ungroup() |> 
  gt(groupname_col = "outcome") |> 
  cols_move_to_start(columns = c(exposure_label, adjustment)) |> 
  tab_spanner(
    label = "Doctors",
    columns = starts_with("Doc")
    ) |>
  tab_spanner(
    label = "Nurses",
    columns = starts_with("Nur")
    ) |>
  tab_spanner(
    label = "Aggregate",
    columns = starts_with("Agg")
    ) |>
  cols_label(
    exposure_label = "",
    adjustment = "",
    Doctor_max_rhat = "Max R-hat",
    Doctor_min_ess_bulk = "Min ESS",
    Doctor_divergences = "Div.",
    Nurse_max_rhat = "Max R-hat",
    Nurse_min_ess_bulk = "Min ESS",
    Nurse_divergences = "Div.",
    Aggregate_max_rhat = "Max R-hat",
    Aggregate_min_ess_bulk = "Min ESS",
    Aggregate_divergences = "Div.",
    ) |>
  fmt_number(
    columns = ends_with("rhat"),
    decimals = 2
    ) |>
  fmt_integer(
    columns = ends_with("bulk")
    ) |>
  tab_header(
    title = "Bayes' Diagnostics for weakly informative priors"
    ) |>
  tab_options(
    row_group.font.weight = "bold",
    column_labels.font.weight = "bold",
    table.font.size = 10,
    data_row.padding = px(2)
    ) |> 
  tab_footnote(
    locations = cells_column_spanners(spanners = contains("Agg")),
    footnote = "Aggregate models only performed for violence exposures and depression/anxiety."
      ) |> 
  tab_footnote(
    locations = cells_column_labels(columns = contains("rhat")),
    footnote = md("*Gelman-Rubin diagnostic*. Maximum value for R-hat in model estimates, < 1.01 shows good convergence.")
    ) |>
  tab_footnote(
    locations = cells_column_labels(columns = contains("bulk")),
    footnote = md("*Effective Sample Size*. Minimum value for ESS in model estimates. A value > 400 is sufficient for stable estimations.")
    ) |> 
  tab_footnote(
    locations = cells_column_labels(columns = contains("divergences")),
    footnote = md("*Divergences*. No divergences show good model convergence.")
    ) |>
    cols_hide(columns = "exposure")


tbl_main_dx

Bayes' Diagnostics for weakly informative priors
		Doctors			Nurses			Aggregate1
		Max R-hat2	Min ESS3	Div.4	Max R-hat2	Min ESS3	Div.4	Max R-hat2	Min ESS3	Div.4
Depression
Bullying	Crude	1.01	914	0	1.01	845	0	NA	NA	NA
	Partially adjusted	1.00	966	0	1.01	1,100	0	NA	NA	NA
	Fully adjusted	1.01	1,030	0	1.00	931	0	1.00	1,030	0
Sexual Harassment	Crude	1.01	964	0	1.00	981	0	NA	NA	NA
	Partially adjusted	1.01	978	0	1.00	1,052	0	NA	NA	NA
	Fully adjusted	1.01	1,021	0	1.00	1,108	0	1.00	1,115	0
Violent threats	Crude	1.01	798	0	1.00	998	0	NA	NA	NA
	Partially adjusted	1.00	1,086	0	1.01	937	0	NA	NA	NA
	Fully adjusted	1.00	967	0	1.00	1,117	0	1.00	1,094	0
Physical violence	Crude	1.00	1,077	0	1.00	968	0	NA	NA	NA
	Partially adjusted	1.00	1,261	0	1.00	964	0	NA	NA	NA
	Fully adjusted	1.00	1,074	0	1.00	995	0	1.00	958	0
Colleague support	Crude	1.00	879	0	1.00	890	0	NA	NA	NA
	Partially adjusted	1.00	851	0	1.00	1,032	0	NA	NA	NA
	Fully adjusted	1.00	1,139	0	1.00	1,192	0	NA	NA	NA
Support from superiors	Crude	1.01	821	0	1.00	1,054	0	NA	NA	NA
	Partially adjusted	1.00	1,074	0	1.01	1,027	0	NA	NA	NA
	Fully adjusted	1.00	876	0	1.00	1,209	0	NA	NA	NA
Meaning in work	Crude	1.00	967	0	1.00	996	0	NA	NA	NA
	Partially adjusted	1.00	997	0	1.01	950	0	NA	NA	NA
	Fully adjusted	1.00	840	0	1.00	950	0	NA	NA	NA
Purpose in work	Crude	1.00	930	0	1.00	1,045	0	NA	NA	NA
	Partially adjusted	1.01	971	0	1.01	1,142	0	NA	NA	NA
	Fully adjusted	1.00	1,007	0	1.00	1,217	0	NA	NA	NA
Anxiety
Bullying	Crude	1.00	1,130	0	1.00	901	0	NA	NA	NA
	Partially adjusted	1.00	1,010	0	1.00	827	0	NA	NA	NA
	Fully adjusted	1.00	1,112	0	1.00	800	0	1.00	1,226	0
Sexual Harassment	Crude	1.00	1,000	0	1.00	1,054	0	NA	NA	NA
	Partially adjusted	1.00	1,128	0	1.00	1,193	0	NA	NA	NA
	Fully adjusted	1.00	1,072	0	1.00	1,101	0	1.00	1,010	0
Violent threats	Crude	1.00	1,282	0	1.01	953	0	NA	NA	NA
	Partially adjusted	1.01	1,356	0	1.01	1,083	0	NA	NA	NA
	Fully adjusted	1.00	1,155	0	1.01	1,020	0	1.00	986	0
Physical violence	Crude	1.00	978	0	1.01	1,038	0	NA	NA	NA
	Partially adjusted	1.01	959	0	1.01	943	0	NA	NA	NA
	Fully adjusted	1.00	1,069	0	1.00	1,069	0	1.00	991	0
Colleague support	Crude	1.01	1,060	0	1.01	1,000	0	NA	NA	NA
	Partially adjusted	1.01	1,083	0	1.00	923	0	NA	NA	NA
	Fully adjusted	1.00	1,009	0	1.00	1,060	0	NA	NA	NA
Support from superiors	Crude	1.01	879	0	1.00	913	0	NA	NA	NA
	Partially adjusted	1.00	1,004	0	1.00	1,080	0	NA	NA	NA
	Fully adjusted	1.00	1,130	0	1.01	1,197	0	NA	NA	NA
Meaning in work	Crude	1.00	955	0	1.00	1,080	0	NA	NA	NA
	Partially adjusted	1.01	1,235	0	1.01	1,129	0	NA	NA	NA
	Fully adjusted	1.00	1,134	0	1.00	1,102	0	NA	NA	NA
Purpose in work	Crude	1.01	1,029	0	1.00	1,183	0	NA	NA	NA
	Partially adjusted	1.00	936	0	1.01	1,201	0	NA	NA	NA
	Fully adjusted	1.01	1,221	0	1.00	1,055	0	NA	NA	NA
Suicide thoughts
Bullying	Crude	1.00	1,356	0	1.00	1,091	0	NA	NA	NA
	Partially adjusted	1.00	1,092	0	1.00	1,116	0	NA	NA	NA
	Fully adjusted	1.00	1,183	0	1.01	1,044	0	NA	NA	NA
Sexual Harassment	Crude	1.00	993	0	1.01	1,202	0	NA	NA	NA
	Partially adjusted	1.00	1,169	0	1.00	953	0	NA	NA	NA
	Fully adjusted	1.00	1,317	0	1.00	918	0	NA	NA	NA
Violent threats	Crude	1.01	1,302	0	1.00	1,039	0	NA	NA	NA
	Partially adjusted	1.01	1,327	0	1.01	1,059	0	NA	NA	NA
	Fully adjusted	1.01	1,572	1	1.00	1,125	0	NA	NA	NA
Physical violence	Crude	1.01	1,099	1	1.00	1,075	0	NA	NA	NA
	Partially adjusted	1.00	1,152	0	1.00	1,076	0	NA	NA	NA
	Fully adjusted	1.00	1,309	0	1.00	1,133	0	NA	NA	NA
Colleague support	Crude	1.00	1,023	0	1.00	886	0	NA	NA	NA
	Partially adjusted	1.00	1,052	0	1.01	1,174	0	NA	NA	NA
	Fully adjusted	1.00	1,314	0	1.00	974	0	NA	NA	NA
Support from superiors	Crude	1.00	1,248	0	1.00	1,040	0	NA	NA	NA
	Partially adjusted	1.00	1,202	0	1.00	1,040	0	NA	NA	NA
	Fully adjusted	1.00	1,216	0	1.00	1,285	0	NA	NA	NA
Meaning in work	Crude	1.00	1,125	0	1.01	1,010	0	NA	NA	NA
	Partially adjusted	1.00	1,103	0	1.00	970	0	NA	NA	NA
	Fully adjusted	1.01	1,023	1	1.00	1,247	0	NA	NA	NA
Purpose in work	Crude	1.01	1,133	0	1.00	1,132	0	NA	NA	NA
	Partially adjusted	1.00	1,092	0	1.00	1,046	0	NA	NA	NA
	Fully adjusted	1.00	1,027	0	1.01	1,182	0	NA	NA	NA
1 Aggregate models only performed for violence exposures and depression/anxiety.
2 Gelman-Rubin diagnostic. Maximum value for R-hat in model estimates, < 1.01 shows good convergence.
3 Effective Sample Size. Minimum value for ESS in model estimates. A value > 400 is sufficient for stable estimations.
4 Divergences. No divergences show good model convergence.

gtsave(tbl_main_dx, "out/tables/diagnostics_tbl_main_bayes.docx")


tbl_sens_dx <-
  data_sens |>
  mutate(outcome = factor(outcome, levels = outcome_order),
         adjustment = factor(adjustment, levels = adjustment_order),
         exposure = factor(exposure, levels = exposure_order)
         ) |>
  arrange(outcome, exposure, adjustment) |>
  group_by(outcome, exposure) |> 
  mutate(
    exposure_label = ifelse(row_number() == 1, as.character(exposure), "") 
  ) |> 
  ungroup() |> 
  gt(groupname_col = "outcome") |> 
  cols_move_to_start(columns = c(exposure_label, adjustment)) |>  
  tab_spanner(
    label = "Doctors",
    columns = starts_with("Doc")
    ) |>
  tab_spanner(
    label = "Nurses",
    columns = starts_with("Nur")
    ) |>
  cols_label(
    exposure_label = "",
    adjustment = "",
    Doctor_max_rhat = "Max R-hat",
    Doctor_min_ess_bulk = "Min ESS",
    Doctor_divergences = "Div.",
    Nurse_max_rhat = "Max R-hat",
    Nurse_min_ess_bulk = "Min ESS",
    Nurse_divergences = "Div."
    ) |>
  fmt_number(
    columns = ends_with("rhat"),
    decimals = 2
    ) |>
  fmt_integer(
    columns = ends_with("bulk")
    ) |>
  tab_header(
    title = "Bayes' Diagnostics for restrictive priors"
    ) |>
  tab_options(
    row_group.font.weight = "bold",
    column_labels.font.weight = "bold",
    table.font.size = 10,
    data_row.padding = px(2)
    ) |> 
  tab_footnote(
    locations = cells_column_spanners(spanners = contains("Agg")),
    footnote = "Aggregate models only performed for violence exposures and depression/anxiety."
      ) |> 
  tab_footnote(
    locations = cells_column_labels(columns = contains("rhat")),
    footnote = md("*Gelman-Rubin diagnostic*. Maximum value for R-hat in model estimates, < 1.01 shows good convergence.")
    ) |>
  tab_footnote(
    locations = cells_column_labels(columns = contains("bulk")),
    footnote = md("*Effective Sample Size*. Minimum value for ESS in model estimates. A value > 400 is sufficient for stable estimations.")
    ) |> 
  tab_footnote(
    locations = cells_column_labels(columns = contains("divergences")),
    footnote = md("*Divergences*. No divergences show good model convergence.")
    ) |>
    cols_hide(columns = "exposure")

tbl_sens_dx

Bayes' Diagnostics for restrictive priors
		Doctors			Nurses
		Max R-hat1	Min ESS2	Div.3	Max R-hat1	Min ESS2	Div.3
Depression
Bullying	Crude	1.01	804	0	1.00	1,198	0
	Partially adjusted	1.01	922	0	1.00	1,030	0
	Fully adjusted	1.00	1,001	0	1.00	1,046	0
Sexual Harassment	Crude	1.01	779	0	1.00	1,005	0
	Partially adjusted	1.00	893	0	1.00	855	0
	Fully adjusted	1.00	896	0	1.00	925	0
Violent threats	Crude	1.00	1,003	0	1.00	1,202	0
	Partially adjusted	1.00	876	0	1.00	1,187	0
	Fully adjusted	1.01	933	0	1.00	904	0
Physical violence	Crude	1.00	1,191	0	1.00	1,012	0
	Partially adjusted	1.00	1,073	0	1.00	1,125	0
	Fully adjusted	1.00	1,123	0	1.00	1,165	0
Colleague support	Crude	1.00	853	0	1.01	908	0
	Partially adjusted	1.01	1,093	0	1.00	1,120	0
	Fully adjusted	1.00	834	0	1.01	1,140	0
Support from superiors	Crude	1.01	855	0	1.00	1,053	0
	Partially adjusted	1.00	958	0	1.00	977	0
	Fully adjusted	1.00	865	0	1.01	975	0
Meaning in work	Crude	1.00	1,111	0	1.00	985	0
	Partially adjusted	1.01	967	0	1.00	1,025	0
	Fully adjusted	1.00	1,086	0	1.00	1,027	0
Purpose in work	Crude	1.00	879	0	1.00	1,134	0
	Partially adjusted	1.00	927	0	1.00	1,077	0
	Fully adjusted	1.00	1,120	0	1.00	1,260	0
Anxiety
Bullying	Crude	1.00	1,206	0	1.00	1,064	0
	Partially adjusted	1.00	1,071	0	1.01	1,048	0
	Fully adjusted	1.00	891	0	1.00	955	0
Sexual Harassment	Crude	1.01	934	0	1.00	862	0
	Partially adjusted	1.00	1,039	0	1.00	988	0
	Fully adjusted	1.01	1,160	0	1.00	1,098	0
Violent threats	Crude	1.00	957	0	1.00	959	0
	Partially adjusted	1.00	1,219	0	1.00	906	0
	Fully adjusted	1.00	1,149	0	1.00	1,162	0
Physical violence	Crude	1.00	941	0	1.00	904	0
	Partially adjusted	1.00	916	0	1.00	964	0
	Fully adjusted	1.00	984	0	1.00	1,194	0
Colleague support	Crude	1.00	1,064	0	1.00	991	0
	Partially adjusted	1.00	849	0	1.01	1,184	0
	Fully adjusted	1.00	1,104	0	1.00	1,065	0
Support from superiors	Crude	1.00	954	0	1.00	1,050	0
	Partially adjusted	1.00	1,014	0	1.00	1,075	0
	Fully adjusted	1.00	1,126	0	1.00	1,182	0
Meaning in work	Crude	1.00	1,067	0	1.00	1,156	0
	Partially adjusted	1.00	1,072	0	1.00	1,016	0
	Fully adjusted	1.00	1,158	0	1.00	1,299	0
Purpose in work	Crude	1.00	1,077	0	1.00	1,135	0
	Partially adjusted	1.00	969	0	1.00	1,124	0
	Fully adjusted	1.00	1,183	0	1.00	1,075	0
1 Gelman-Rubin diagnostic. Maximum value for R-hat in model estimates, < 1.01 shows good convergence.
2 Effective Sample Size. Minimum value for ESS in model estimates. A value > 400 is sufficient for stable estimations.
3 Divergences. No divergences show good model convergence.

gtsave(tbl_sens_dx, "out/tables/diagnostics_tbl_sens_bayes.docx")

Absolute risks

brm_results_df <- readRDS("out/dataframes/brm_results.rds")

survey_results_df <- readRDS("out/dataframes/svy_results_df.rds")

glm_results_df <- readRDS("out/dataframes/glm_multilevel_df.rds")

# clean
brm_risks <- 
  brm_results_df |> 
  filter(Model == "Fully adjusted") |> 
  select(Outcome, Exposure, Profession, Risk_Unexposed, Risk_Exposed)|>
  rename(Unexposed_Bayesian = Risk_Unexposed,
         Exposed_Bayesian = Risk_Exposed) |> 
  mutate(Exposed_Bayesian = as.numeric(str_replace(Exposed_Bayesian, "%",
                                                   "")),
         Unexposed_Bayesian = as.numeric(str_replace(Unexposed_Bayesian, "%",
                                                   ""))) |> 
  mutate(ARR_Bayesian = abs(Exposed_Bayesian - Unexposed_Bayesian)) |> 
  mutate(Exposure = case_when(
    Exposure == "Harassment" ~ "Sexual Harassment",
    TRUE ~ Exposure
  ))



svy_risks <-
  survey_results_df |> 
  filter(Model == "Fully Adj.") |> 
  select(Outcome, Exposure, Profession, Risk_Unexposed, Risk_Exposed) |> 
  rename(Unexposed_Survey = Risk_Unexposed,
         Exposed_Survey = Risk_Exposed) |> 
  mutate(Exposed_Survey = as.numeric(str_replace(Exposed_Survey, "%",
                                                   "")),
         Unexposed_Survey = as.numeric(str_replace(Unexposed_Survey, "%",
                                                   ""))) |> 
  mutate(ARR_Survey = abs(Exposed_Survey - Unexposed_Survey))


glm_risks <- 
  glm_results_df |> 
  filter(Adjustment == "Fully adjusted") |>
  select(Outcome, Exposure, Profession, Risk_Unexp, Risk_Exp) |> 
  rename(Unexposed_GLM = Risk_Unexp,
         Exposed_GLM = Risk_Exp) |> 
  mutate(Exposed_GLM = as.numeric(str_replace(Exposed_GLM, "%",
                                                   "")),
         Unexposed_GLM = as.numeric(str_replace(Unexposed_GLM, "%",
                                                   ""))) |> 
  mutate(ARR_GLM = abs(Exposed_GLM - Unexposed_GLM)) |> 
   mutate(
    Exposure = case_when(
      Exposure == "work_21_dic" ~ "Colleague support",
      Exposure == "work_22_dic" ~ "Support from superiors",
      Exposure == "work_31_dic" ~ "Violent threats",
      Exposure == "work_32_dic" ~ "Physical violence",
      Exposure == "work_30_dic" ~ "Sexual Harassment",
      Exposure == "work_33_dic" ~ "Bullying",
      Exposure == "wb_wami_1_dic" ~ "Meaning in work",
      Exposure == "wb_wami_2_dic" ~ "Purpose in work",
      TRUE ~ Exposure),
    Outcome = case_when(
      Outcome == "phq_co" ~ "Depression",
      Outcome == "gad_co" ~ "Anxiety",
      Outcome == "suic_idea" ~ "Suicide thoughts",
      TRUE ~ Outcome
    )
   )



exposures_risks <-
  brm_risks |> 
  left_join(svy_risks, by = c("Outcome", "Exposure", "Profession")) |> 
  left_join(glm_risks, by = c("Outcome", "Exposure", "Profession"))
  

exposures_risks

## # A tibble: 48 × 12
##    Outcome Exposure  Profession Unexposed_Bayesian Exposed_Bayesian ARR_Bayesian
##    <chr>   <chr>     <chr>                   <dbl>            <dbl>        <dbl>
##  1 Anxiety Bullying  Doctor                   18.3             42.3         24  
##  2 Anxiety Bullying  Nurse                    12.5             35.1         22.6
##  3 Anxiety Colleagu… Doctor                   32               19.9         12.1
##  4 Anxiety Colleagu… Nurse                    22.2             12.6          9.6
##  5 Anxiety Sexual H… Doctor                   22.7             44.2         21.5
##  6 Anxiety Sexual H… Nurse                    15.1             38.5         23.4
##  7 Anxiety Meaning … Doctor                   41               19.4         21.6
##  8 Anxiety Meaning … Nurse                    26.5             12.2         14.3
##  9 Anxiety Purpose … Doctor                   46.6             20.5         26.1
## 10 Anxiety Purpose … Nurse                    28.4             13.9         14.5
## # ℹ 38 more rows
## # ℹ 6 more variables: Unexposed_Survey <dbl>, Exposed_Survey <dbl>,
## #   ARR_Survey <dbl>, Unexposed_GLM <dbl>, Exposed_GLM <dbl>, ARR_GLM <dbl>

arr_tbl <-

  glm_risks |> 
  pivot_wider(
    names_from = Profession,
    values_from = c("Unexposed_GLM", "Exposed_GLM", "ARR_GLM")
  ) |> 
  mutate(
    across(
      where(is.numeric),
      ~ as.character(paste0(round(.x, 1), "%"))
    )
  ) |> 
  mutate(Outcome = factor(Outcome, levels = outcome_order), 
         Exposure = factor(Exposure, levels = exposure_order)
         ) |>
  arrange(Outcome, Exposure) |> 
  gt(groupname_col = "Outcome",
     rowname_col = "Exposure") |>
  tab_header(title = md("**Absolute prevalence and prevalence difference for each exposure-outcome pair**")) |>
      tab_spanner(
        label = md("**Doctor**"),
        columns = ends_with("Doctor")
      ) |> 
      tab_spanner(
        label = md("**Nurse**"),
        columns = ends_with("Nurse")
      ) |> 
      cols_label(
        starts_with("Unexposed") ~ "Unexposed",
        starts_with("Exposed") ~ "Exposed",
        starts_with("ARR") ~ "Difference"
      ) |> 
      cols_align(
        align = "center",
        columns = contains(c("_Doctor", "_Nurse"))
      ) |> 
      tab_style(
        style = cell_text(align = "left"),
        locations = cells_stub()
        ) |>
      tab_stub_indent(
        rows = everything(), 
        indent = 3
      ) |> 
      tab_footnote(
        locations = cells_column_labels(
          columns = matches("ARR")
        ),
        footnote = md("*Prevalence difference*")
      ) |>
      tab_footnote(footnote = "Estimates from fully adjusted multilevel models.")


arr_tbl

Absolute prevalence and prevalence difference for each exposure-outcome pair
	Doctor			Nurse
	Unexposed	Exposed	Difference1	Unexposed	Exposed	Difference1
Depression
Bullying	27.4%	55.3%	27.9%	17.9%	44.6%	26.7%
Sexual Harassment	33.6%	50%	16.4%	21.4%	44.5%	23.1%
Violent threats	30%	55.3%	25.3%	20.4%	43.4%	23%
Physical violence	34.9%	52.1%	17.2%	22.3%	45.2%	22.9%
Colleague support	44.4%	29.2%	15.2%	30.3%	18%	12.3%
Support from superiors	43.9%	27.1%	16.8%	30.5%	15.8%	14.7%
Meaning in work	55%	27.5%	27.5%	37.3%	16.3%	21%
Purpose in work	60.9%	29.4%	31.5%	39.4%	19.3%	20.1%
Anxiety
Bullying	18.8%	39.5%	20.7%	12.7%	33.7%	21%
Sexual Harassment	23.2%	37.6%	14.4%	15.3%	34.9%	19.6%
Violent threats	20.7%	39.9%	19.2%	14.6%	33.1%	18.5%
Physical violence	24.2%	43.3%	19.1%	16.1%	34.5%	18.4%
Colleague support	31.2%	20.4%	10.8%	22.2%	12.8%	9.4%
Support from superiors	31.3%	18.6%	12.7%	22.2%	11.4%	10.8%
Meaning in work	39.6%	19.6%	20%	26.3%	12.3%	14%
Purpose in work	44.3%	20.7%	23.6%	27.8%	14.2%	13.6%
Suicide thoughts
Bullying	9.1%	20.2%	11.1%	5.9%	18.4%	12.5%
Sexual Harassment	11%	23.4%	12.4%	7.4%	20.5%	13.1%
Violent threats	9.8%	21.3%	11.5%	7%	18%	11%
Physical violence	11.7%	27.8%	16.1%	7.9%	21.1%	13.2%
Colleague support	15.7%	10.1%	5.6%	11.6%	6.1%	5.5%
Support from superiors	15.4%	9.4%	6%	10.9%	6%	4.9%
Meaning in work	20.9%	9.2%	11.7%	12.8%	6%	6.8%
Purpose in work	24.9%	9.7%	15.2%	15.2%	6.7%	8.5%
Estimates from fully adjusted multilevel models.
1 Prevalence difference

gtsave(arr_tbl, "out/dataframes/arr_table_long.docx")

Sensitivity forest plot

brm_res <- readRDS("out/dataframes/brm_results.rds") |> 
  filter(Type == "PR", Model == "Fully adjusted") |> 
  select(Outcome, Exposure, Profession, Est_CI) |> 
  mutate(Analysis = "Bayesian main (Weakly informative priors)")


brm_sens <- readRDS("out/dataframes/bay_res_sens.rds") |> 
  mutate(
    Analysis = "Bayesian sensitivity (Restrictive priors)")
  

svy_res <- readRDS("out/dataframes/svy_results_df.rds") |> 
  filter(Type == "PR", Model == "Fully Adj.") |> 
  select(-c(Model, Type), -contains("Risk_")) |> 
  mutate(Analysis = "Survey design regression")

glm_res <- readRDS("out/dataframes/glm_results_df.rds") |> 
  filter(Adjustment == "Fully adjusted") |>
  select(Outcome, Exposure, Profession, Est_CI) |> 
  mutate(Analysis = "GLM")


sens_df <-
  brm_res |> 
  bind_rows(brm_sens, svy_res, glm_res) |> 
  mutate(Outcome = case_when(
    str_detect(Outcome, "Ideation") ~ "Suicide thoughts",
    TRUE ~ Outcome),
    Exposure = if_else(Exposure == "Harassment", 
                       "Sexual Harassment",
                       Exposure))
  
sens_df

## # A tibble: 176 × 5
##    Outcome Exposure          Profession Est_CI            Analysis              
##    <chr>   <chr>             <chr>      <chr>             <chr>                 
##  1 Anxiety Bullying          Doctor     2.09 (1.91, 2.28) Bayesian main (Weakly…
##  2 Anxiety Bullying          Nurse      2.64 (2.40, 2.92) Bayesian main (Weakly…
##  3 Anxiety Colleague support Doctor     0.65 (0.60, 0.71) Bayesian main (Weakly…
##  4 Anxiety Colleague support Nurse      0.58 (0.53, 0.63) Bayesian main (Weakly…
##  5 Anxiety Sexual Harassment Doctor     1.61 (1.42, 1.81) Bayesian main (Weakly…
##  6 Anxiety Sexual Harassment Nurse      2.25 (1.97, 2.57) Bayesian main (Weakly…
##  7 Anxiety Meaning in work   Doctor     0.50 (0.45, 0.56) Bayesian main (Weakly…
##  8 Anxiety Meaning in work   Nurse      0.47 (0.42, 0.53) Bayesian main (Weakly…
##  9 Anxiety Purpose in work   Doctor     0.47 (0.42, 0.53) Bayesian main (Weakly…
## 10 Anxiety Purpose in work   Nurse      0.51 (0.45, 0.59) Bayesian main (Weakly…
## # ℹ 166 more rows

forest_sens_comp <-
  
  sens_df |>
    filter(Outcome != "Suicide thoughts") |> 
  # extract numeric estimates for plotting
  mutate(
    PR = as.numeric(str_extract(Est_CI, "^[0-9\\.]+")),
    Lower = as.numeric(str_extract(Est_CI, "(?<=\\()[0-9\\.]+")),
    Upper = as.numeric(str_extract(Est_CI, "[0-9\\.]+(?=\\))"))
  ) |>
  mutate(Outcome = factor(Outcome, levels = c("Depression", "Anxiety")),
         Exposure = factor(Exposure, levels = rev(exposure_order)),
         Analysis = factor(
           Analysis,
           levels = c("GLM",
                      "Survey design regression",
                      "Bayesian main (Weakly informative priors)",
                      "Bayesian sensitivity (Restrictive priors)"))) |>
  arrange(Outcome, Exposure, Analysis) |> 
  ggplot(aes(x = PR, 
             y = Exposure, 
             shape = Analysis)) +
  geom_point(position = position_dodge(width = 0.5), 
             size = 3) +
  geom_errorbar(aes(xmin = Lower, 
                    xmax = Upper), 
                 position = position_dodge(width = 0.5)) +
  facet_wrap(~Profession + Outcome, 
             scales = "free_y") +
  geom_vline(xintercept = 1, 
             linetype = "dashed") +
  scale_x_log10(breaks = c(0.2, 0.5, 1, 2, 4), 
                labels = scales::label_number(
                  accuracy = 0.1,
                  decimal.mark = "·"
                )) +
  theme(
    legend.text = element_text(size = 10),
    legend.margin = margin(b = -5), # pulls legend close to plot
    plot.margin = margin(0, 0.1, 0, 0, "cm"),
    panel.spacing.x = unit(0.5, "cm"),
    strip.text = element_text(size = 10),
    axis.title.x = element_text(size = 10),
    axis.text.x = element_text(size = 10),
    axis.title.y = element_text(size = 10),
    axis.text.y = element_text(size = 10)
  ) +
  ggpubr::theme_pubr()


forest_sens_comp

ggsave(filename = "forest_sens_comparison.pdf",
       plot = forest_sens_comp,
       path = "out/plots/",
       width = 210,
       height = 170,
       units = "mm",
       dpi = 320,
       scale = 2.2
       )


forest_sens_suic <-
  
  sens_df |>
    filter(Outcome == "Suicide thoughts") |> 
  # extract numeric estimates for plotting
  mutate(
    PR = as.numeric(str_extract(Est_CI, "^[0-9\\.]+")),
    Lower = as.numeric(str_extract(Est_CI, "(?<=\\()[0-9\\.]+")),
    Upper = as.numeric(str_extract(Est_CI, "[0-9\\.]+(?=\\))"))
  ) |> 
    mutate(Exposure = factor(Exposure, levels = rev(exposure_order)),
           Analysis = factor(
             Analysis,
             levels = c("GLM",
                        "Survey design regression",
                        "Bayesian main (Weakly informative priors)",
                        "Bayesian sensitivity (Restrictive priors)"))) |> 
  ggplot(aes(x = PR, 
             y = Exposure,
             shape = Analysis)) +
  geom_point(position = position_dodge(width = 0.5), size = 3) +
  geom_errorbar(aes(xmin = Lower, xmax = Upper), 
                 position = position_dodge(width = 0.5)) +
  facet_wrap(~Profession + Outcome, scales = "free_y") +
  geom_vline(xintercept = 1, linetype = "dashed") +
  scale_x_log10(breaks = c(0.2, 0.5, 1, 2, 4), 
                labels = scales::label_number(
                  accuracy = 0.1,
                  decimal.mark = "·"
                )) +
  theme(
    legend.text = element_text(size = 10),
    legend.margin = margin(b = -5), # pulls legend close to plot
    plot.margin = margin(0, 0.1, 0, 0, "cm"),
    panel.spacing.x = unit(0.5, "cm"),
    strip.text = element_text(size = 10),
    axis.title.x = element_text(size = 10),
    axis.text.x = element_text(size = 10),
    axis.title.y = element_text(size = 10),
    axis.text.y = element_text(size = 10)
  ) +
  ggpubr::theme_pubr() 

forest_sens_suic

ggsave(filename = "forest_sens_comparison_suic.pdf",
       plot = forest_sens_suic,
       path = "out/plots/",
       width = 210,
       height = 170,
       units = "mm",
       dpi = 320,
       scale = 1.5
       )

Sensitivity table

tbl_sensitivity_all <-
  
  sens_df |>
  select(Outcome, Exposure, Profession, Analysis, Est_CI) |> 
  # Pivot to wide format
  pivot_wider(
    names_from = c(Profession, Analysis),
    values_from = Est_CI,
    names_sep = "_"
  ) |>
  # Order columns logically
  select(Outcome, Exposure, 
         contains("Doctor_GLM"), contains("Doctor_Survey"),  
         contains("Doctor_Bayesian main"), contains("Doctor_Bayesian sens"), 
         contains("Nurse_GLM"), contains("Nurse_Survey"),
         contains("Nurse_Bayesian main"), contains("Nurse_Bayesian sens") 
         ) |>
  mutate(Outcome = factor(Outcome, levels = outcome_order), 
         Exposure = factor(Exposure, levels = exposure_order)) |>
  arrange(Outcome, Exposure) |> 
  # Create Table
  gt(groupname_col = "Outcome", rowname_col = "Exposure") |>
  
  # Headers
  tab_header(
    title = md("**Sensitivity Analysis: All methods**"),
    subtitle = "Fully Adjusted Prevalence Ratios (95% CI or CrI)"
  ) |>
  
  # Spanners for Profession
  tab_spanner(
    label = md("**Doctor**"),
    columns = starts_with("Doctor")
  ) |>
  tab_spanner(
    label = md("**Nurse**"),
    columns = starts_with("Nurse")
  ) |>
  
  # Clean Column Labels (Remove the "Doctor_" prefix)
  cols_label(
    `Doctor_Bayesian main (Weakly informative priors)` = 
      "Bayesian (Weak priors)",
    `Doctor_Bayesian sensitivity (Restrictive priors)` = 
      "Bayesian (Restrictive)",
    `Doctor_Survey design regression` = "Survey design",
    `Doctor_GLM` = "GLM Multilevel",
    `Nurse_Bayesian main (Weakly informative priors)` = 
      "Bayesian (Weak priors)",
    `Nurse_Bayesian sensitivity (Restrictive priors)` = 
      "Bayesian (Restrictive)",
    `Nurse_Survey design regression` = "Survey design",
    `Nurse_GLM` = "GLM Multilevel",
  ) |>
  
  # Formatting
  cols_align(align = "center", columns = contains("_")) |>
  tab_stub_indent(rows = everything(), indent = 3) |>
  tab_footnote(
    footnote = "Fully adjusted models."
  ) |> 
  tab_footnote(
    footnote = "Multilevel GLM.",
    locations = cells_column_labels(columns = contains("GLM"))
  ) |> 
  tab_footnote(
    footnote ="Survey design applied with multivariate regression.",
    locations = cells_column_labels(columns = contains("Survey"))
  ) |> 
  tab_footnote(
    footnote =  "Bayesian Restrictive: Doctors use informative intercepts based on prevalence (Anxiety and depression, no information on suicide thoughts) + Skeptical Betas; Nurses use Skeptical Betas. ",
    locations = cells_column_labels(columns = contains("Restrictive"))
  )

tbl_sensitivity_all

Sensitivity Analysis: All methods
Fully Adjusted Prevalence Ratios (95% CI or CrI)
	Doctor				Nurse
	GLM Multilevel1	Survey design2	Bayesian (Weak priors)	Bayesian (Restrictive)3	GLM Multilevel1	Survey design2	Bayesian (Weak priors)	Bayesian (Restrictive)3
Depression
Bullying	2.02 (1.88, 2.18)	2.03 (1.93, 2.14)	2.02 (1.87, 2.17)	2.00 (1.86, 2.15)	2.49 (2.30, 2.71)	2.53 (2.26, 2.83)	2.48 (2.28, 2.69)	2.44 (2.26, 2.65)
Sexual Harassment	1.49 (1.34, 1.65)	1.49 (1.39, 1.61)	1.48 (1.34, 1.65)	1.47 (1.33, 1.63)	2.08 (1.86, 2.33)	2.11 (1.91, 2.33)	2.06 (1.84, 2.31)	2.03 (1.81, 2.27)
Violent threats	1.85 (1.71, 2.00)	1.86 (1.75, 1.98)	1.84 (1.70, 1.99)	1.83 (1.69, 1.97)	2.12 (1.93, 2.34)	2.17 (2.05, 2.31)	2.11 (1.91, 2.32)	2.07 (1.88, 2.29)
Physical violence	1.49 (1.27, 1.76)	1.50 (1.30, 1.72)	1.48 (1.25, 1.73)	1.44 (1.23, 1.67)	2.02 (1.74, 2.36)	2.08 (1.87, 2.33)	1.99 (1.69, 2.33)	1.91 (1.65, 2.21)
Colleague support	0.66 (0.61, 0.71)	0.66 (0.61, 0.71)	0.66 (0.61, 0.71)	0.66 (0.62, 0.71)	0.59 (0.55, 0.64)	0.60 (0.55, 0.64)	0.60 (0.55, 0.65)	0.60 (0.56, 0.65)
Support from superiors	0.62 (0.57, 0.67)	0.62 (0.57, 0.67)	0.62 (0.57, 0.67)	0.62 (0.58, 0.67)	0.52 (0.48, 0.56)	0.52 (0.47, 0.57)	0.52 (0.48, 0.56)	0.52 (0.48, 0.57)
Meaning in work	0.50 (0.46, 0.55)	0.50 (0.47, 0.53)	0.50 (0.46, 0.55)	0.51 (0.47, 0.56)	0.44 (0.40, 0.48)	0.44 (0.40, 0.49)	0.44 (0.40, 0.48)	0.45 (0.41, 0.49)
Purpose in work	0.48 (0.44, 0.53)	0.48 (0.45, 0.51)	0.48 (0.44, 0.54)	0.49 (0.45, 0.55)	0.49 (0.44, 0.55)	0.49 (0.45, 0.53)	0.49 (0.44, 0.55)	0.50 (0.45, 0.56)
Anxiety
Bullying	2.10 (1.92, 2.29)	2.11 (1.96, 2.28)	2.09 (1.91, 2.28)	2.06 (1.89, 2.25)	2.66 (2.42, 2.93)	2.69 (2.42, 2.99)	2.64 (2.40, 2.92)	2.58 (2.34, 2.84)
Sexual Harassment	1.62 (1.44, 1.82)	1.62 (1.49, 1.77)	1.61 (1.42, 1.81)	1.59 (1.42, 1.78)	2.28 (2.00, 2.60)	2.30 (1.99, 2.65)	2.25 (1.97, 2.57)	2.18 (1.91, 2.48)
Violent threats	1.93 (1.76, 2.12)	1.94 (1.77, 2.11)	1.92 (1.75, 2.11)	1.89 (1.72, 2.08)	2.27 (2.03, 2.54)	2.31 (2.17, 2.47)	2.25 (1.99, 2.52)	2.19 (1.96, 2.43)
Physical violence	1.79 (1.49, 2.15)	1.81 (1.60, 2.04)	1.75 (1.46, 2.09)	1.67 (1.39, 1.98)	2.14 (1.80, 2.56)	2.18 (1.84, 2.58)	2.08 (1.75, 2.46)	1.96 (1.65, 2.32)
Colleague support	0.65 (0.60, 0.71)	0.65 (0.61, 0.69)	0.65 (0.60, 0.71)	0.66 (0.61, 0.72)	0.57 (0.52, 0.63)	0.58 (0.52, 0.64)	0.58 (0.53, 0.63)	0.58 (0.53, 0.64)
Support from superiors	0.60 (0.54, 0.65)	0.59 (0.55, 0.64)	0.60 (0.54, 0.65)	0.60 (0.55, 0.65)	0.51 (0.47, 0.57)	0.51 (0.47, 0.57)	0.52 (0.47, 0.57)	0.52 (0.48, 0.57)
Meaning in work	0.50 (0.45, 0.55)	0.49 (0.45, 0.54)	0.50 (0.45, 0.56)	0.51 (0.46, 0.57)	0.47 (0.42, 0.53)	0.47 (0.42, 0.54)	0.47 (0.42, 0.53)	0.49 (0.44, 0.54)
Purpose in work	0.47 (0.41, 0.53)	0.47 (0.42, 0.52)	0.47 (0.42, 0.53)	0.49 (0.43, 0.55)	0.51 (0.45, 0.58)	0.51 (0.45, 0.58)	0.51 (0.45, 0.59)	0.53 (0.47, 0.60)
Suicide thoughts
Bullying	2.22 (1.96, 2.51)	2.22 (1.95, 2.53)	2.20 (1.94, 2.48)	NA	3.11 (2.73, 3.56)	3.17 (2.69, 3.74)	3.05 (2.69, 3.46)	NA
Sexual Harassment	2.13 (1.83, 2.48)	2.15 (1.99, 2.32)	2.10 (1.81, 2.45)	NA	2.78 (2.35, 3.30)	2.80 (2.34, 3.36)	2.72 (2.30, 3.21)	NA
Violent threats	2.18 (1.92, 2.48)	2.18 (1.94, 2.44)	2.16 (1.90, 2.44)	NA	2.56 (2.20, 2.99)	2.63 (2.18, 3.16)	2.52 (2.16, 2.92)	NA
Physical violence	2.37 (1.90, 2.95)	2.35 (1.99, 2.77)	2.26 (1.81, 2.79)	NA	2.68 (2.16, 3.34)	2.74 (2.13, 3.53)	2.56 (2.05, 3.19)	NA
Colleague support	0.64 (0.57, 0.72)	0.64 (0.57, 0.71)	0.64 (0.57, 0.72)	NA	0.52 (0.46, 0.60)	0.52 (0.45, 0.61)	0.53 (0.46, 0.60)	NA
Support from superiors	0.61 (0.54, 0.69)	0.61 (0.53, 0.71)	0.61 (0.54, 0.69)	NA	0.55 (0.48, 0.63)	0.55 (0.47, 0.65)	0.56 (0.49, 0.63)	NA
Meaning in work	0.44 (0.38, 0.51)	0.44 (0.38, 0.51)	0.45 (0.38, 0.52)	NA	0.47 (0.40, 0.55)	0.47 (0.41, 0.54)	0.48 (0.41, 0.56)	NA
Purpose in work	0.39 (0.33, 0.46)	0.39 (0.34, 0.45)	0.40 (0.34, 0.47)	NA	0.44 (0.37, 0.52)	0.44 (0.39, 0.50)	0.45 (0.38, 0.54)	NA
Fully adjusted models.
1 Multilevel GLM.
2 Survey design applied with multivariate regression.
3 Bayesian Restrictive: Doctors use informative intercepts based on prevalence (Anxiety and depression, no information on suicide thoughts) + Skeptical Betas; Nurses use Skeptical Betas.

# Save
gtsave(tbl_sensitivity_all, "out/tables/sensitivity_comparison_all.docx")

Maps

Aggregated prevalences

Exposures and Outcomes

darkblue = "#0045A8"
aqua = "#38aec5"
cyan = "#00C4D6"



# IMPORTANT:
# First time running this code, installation needs to be like this:


# install.packages(
#   "rnaturalearthhires",
#   repos = "https://ropensci.r-universe.dev",
#   type = "source"
# )


ukraine <- rnaturalearth::ne_states(country = "ukraine", 
                                  returnclass = "sf")

# crimea is included as russia (wrong)
crimea <- 
  rnaturalearth::ne_states(country = "russia",
                                   returnclass = "sf") |> 
  filter(name == "Crimea") |> 
  select(fips, geometry)



# get data
exp_reg <- readRDS("out/dataframes/exposure_regions.rds") |> 
  pivot_longer(
    cols = starts_with("work_"),
    names_to = "Exposure",
    values_to = "Prevalence"
  ) |> 
  mutate(Exposure = case_when(
    Exposure == "work_30_dic" ~ "Sexual Harassment",
    Exposure == "work_31_dic" ~ "Violent threats",
    Exposure == "work_32_dic" ~ "Physical violence",
    Exposure == "work_33_dic" ~ "Bullying",
  ))

out_prev_agg <- c("phq_co", "gad_co", "suic_idea")

out_reg <- readRDS("out/dataframes/outcome_regions.rds") |> 
  pivot_longer(
    cols = all_of(out_prev_agg),
    names_to = "Outcome",
    values_to = "Prevalence"
  ) |> 
  mutate(Outcome = case_when(
    Outcome == "phq_co" ~ "Depression",
    Outcome == "gad_co" ~ "Anxiety",
    Outcome == "suic_idea" ~ "Suicide thoughts",
  ))


# join with geometry data
map_info_prev_exp <- 
  ukraine |>
  select(fips, geometry) |>   
  bind_rows(crimea) |> 
  rename(fips_code = fips) |>        
  inner_join(exp_reg, by = "fips_code")

map_info_prev_exp

## Simple feature collection with 104 features and 5 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: 22.13284 ymin: 44.38105 xmax: 40.15954 ymax: 52.36895
## Geodetic CRS:  WGS 84
## First 10 features:
##    fips_code           loc_en                loc_3          Exposure Prevalence
## 1       UP02 Chernihiv Oblast Чернігівська Область Sexual Harassment 0.06405694
## 2       UP02 Chernihiv Oblast Чернігівська Область   Violent threats 0.15995261
## 3       UP02 Chernihiv Oblast Чернігівська Область Physical violence 0.03554502
## 4       UP02 Chernihiv Oblast Чернігівська Область          Bullying 0.23186683
## 5       UP24     Volyn Oblast    Волинська Область Sexual Harassment 0.06364617
## 6       UP24     Volyn Oblast    Волинська Область   Violent threats 0.13852814
## 7       UP24     Volyn Oblast    Волинська Область Physical violence 0.02918919
## 8       UP24     Volyn Oblast    Волинська Область          Bullying 0.22619048
## 9       UP19     Rivne Oblast   Рівненська Область Sexual Harassment 0.04597701
## 10      UP19     Rivne Oblast   Рівненська Область   Violent threats 0.11304348
##                          geometry
## 1  MULTIPOLYGON (((31.47485 52...
## 2  MULTIPOLYGON (((31.47485 52...
## 3  MULTIPOLYGON (((31.47485 52...
## 4  MULTIPOLYGON (((31.47485 52...
## 5  MULTIPOLYGON (((25.1833 51....
## 6  MULTIPOLYGON (((25.1833 51....
## 7  MULTIPOLYGON (((25.1833 51....
## 8  MULTIPOLYGON (((25.1833 51....
## 9  MULTIPOLYGON (((25.5474 51....
## 10 MULTIPOLYGON (((25.5474 51....

map_info_prev_out <- 
  ukraine |>
  select(fips, geometry) |>   
  bind_rows(crimea) |> 
  rename(fips_code = fips) |>        
  inner_join(out_reg, by = "fips_code")

map_info_prev_out

## Simple feature collection with 78 features and 5 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: 22.13284 ymin: 44.38105 xmax: 40.15954 ymax: 52.36895
## Geodetic CRS:  WGS 84
## First 10 features:
##    fips_code           loc_en                loc_3          Outcome Prevalence
## 1       UP02 Chernihiv Oblast Чернігівська Область       Depression 0.32509271
## 2       UP02 Chernihiv Oblast Чернігівська Область          Anxiety 0.24351051
## 3       UP02 Chernihiv Oblast Чернігівська Область Suicide thoughts 0.11960543
## 4       UP24     Volyn Oblast    Волинська Область       Depression 0.27633069
## 5       UP24     Volyn Oblast    Волинська Область          Anxiety 0.16742081
## 6       UP24     Volyn Oblast    Волинська Область Suicide thoughts 0.08558559
## 7       UP19     Rivne Oblast   Рівненська Область       Depression 0.24776119
## 8       UP19     Rivne Oblast   Рівненська Область          Anxiety 0.18154762
## 9       UP19     Rivne Oblast   Рівненська Область Suicide thoughts 0.09523810
## 10      UP27  Zhytomyr Oblast  Житомирська Область       Depression 0.33299697
##                          geometry
## 1  MULTIPOLYGON (((31.47485 52...
## 2  MULTIPOLYGON (((31.47485 52...
## 3  MULTIPOLYGON (((31.47485 52...
## 4  MULTIPOLYGON (((25.1833 51....
## 5  MULTIPOLYGON (((25.1833 51....
## 6  MULTIPOLYGON (((25.1833 51....
## 7  MULTIPOLYGON (((25.5474 51....
## 8  MULTIPOLYGON (((25.5474 51....
## 9  MULTIPOLYGON (((25.5474 51....
## 10 MULTIPOLYGON (((27.66615 51...

# maps
#outcomes

outcomes_gen_map <-
  
  map_info_prev_out |> 
  mutate(Outcome = factor(Outcome, levels = outcome_order)) |>
  arrange(Outcome) |> 
  ggplot() +
  geom_sf(aes(fill = Prevalence), color = "white", size = 0.2) +  
  scale_fill_gradient(low = "#dae6f2",
                      high = darkblue,
                      na.value = "turquoise1",
                      labels = scales::percent,
                      limits = c(0, 0.5)) +
  geom_sf_text(
    aes(
      label = ifelse(
        is.na(Prevalence),
        "ND",
        scales::percent(Prevalence, accuracy = 1)
        )
      ),
    size = 3,
    check_overlap = TRUE
    ) +
  scale_color_manual(values = c("black", "white"), guide = "none") +
  facet_wrap(~Outcome, ncol = 2) +
  labs(
    fill = "Prevalence") +
  ggpubr::theme_pubr() +
  theme(
    legend.position = "right",
    legend.text = element_text(size = 10),
    axis.title = element_blank(), 
    axis.text = element_blank(),  
    axis.ticks = element_blank(),
    axis.line = element_blank(),
    strip.text = element_text(size = 10, face = "bold"),
  ) 

outcomes_gen_map

ggsave(
  "outcomes_map.pdf",
  plot = outcomes_gen_map,
  path = "out/",
  scale = 1.7,
  width = 210,
  height = 160,
  unit = "mm",
  dpi = 350
)


# exposures

exposures_gen_map <-
  map_info_prev_exp |> 
  mutate(Exposure = factor(Exposure, levels = exposure_order)) |> 
  arrange(Exposure) |> 
  ggplot() +
  geom_sf(aes(fill = Prevalence), color = "white", size = 0.2) +  
  scale_fill_gradient(low = "#dae6f2",
                      high = darkblue,
                      na.value = "turquoise1",
                      labels = scales::percent,
                      limits = c(0, 0.5)) +
  geom_sf_text(
    aes(
      label = ifelse(
        is.na(Prevalence),
        "ND",
        scales::percent(Prevalence, accuracy = 1)
        )
      ),
    size = 3,
    check_overlap = TRUE
    ) +
  scale_color_manual(values = c("black", "white"), guide = "none") +
  facet_wrap(~Exposure, ncol = 2) +
  labs(
    fill = "Prevalence") +
  ggpubr::theme_pubr() +
  theme(
    legend.position = "right",
    legend.text = element_text(size = 10),
    axis.title = element_blank(), 
    axis.text = element_blank(),  
    axis.ticks = element_blank(),
    axis.line = element_blank(),
    strip.text = element_text(size = 10, face = "bold"),
  ) 

exposures_gen_map

ggsave(
  "exposures_map.pdf",
  plot = exposures_gen_map,
  path = "out/",
  scale = 1.7,
  width = 210,
  height = 160,
  unit = "mm",
  dpi = 350
)

Aggregated ARs

Depression

# effect of violence on dep
# depression
bay_dep_har_agg_adj <-
  readRDS("out/bayesian_models_agg/bay_dep_har_agg_adj.rds")

bay_dep_bul_agg_adj <-
  readRDS("out/bayesian_models_agg/bay_dep_bul_agg_adj.rds")

bay_dep_threats_agg_adj <-
  readRDS("out/bayesian_models_agg/bay_dep_threats_agg_adj.rds")

bay_dep_viol_agg_adj <-
  readRDS("out/bayesian_models_agg/bay_dep_viol_agg_adj.rds")

reg_dep_har <- 
  
  bay_dep_har_agg_adj |> 
  spread_draws(
    b_Intercept, b_work_30_dicYes, r_loc_3[loc_3, term]
    ) |> 
  mutate(
    region_log_No = b_Intercept + r_loc_3,
    region_log_Yes = b_Intercept + r_loc_3 + b_work_30_dicYes
    ) |>
  mutate(
    prob_dep_har_No = plogis(region_log_No),
    prob_dep_har_Yes = plogis(region_log_Yes)
    ) |> 
  group_by(loc_3) |> 
  summarise(
    No_Har_dep = mean(prob_dep_har_No),
    Yes_Har_dep = mean(prob_dep_har_Yes)
    ) |> 
  mutate(loc_3 = str_replace_all(loc_3, "\\.", " ")) |>
  mutate(loc_3 = case_when(
           loc_3 == "М  Київ" ~ "М. Київ",
           TRUE ~ loc_3
         )) |> 
  add_row(tibble_row(loc_3 = "Ар Крим")) |> 
  left_join(ua_reg_conflict |> select(loc_3, loc_en, fips_code),
            by = "loc_3") |> 
  pivot_longer(
    cols = contains("Har_"),
    names_to = "Category",
    values_to = "Probability"
  ) |> 
  mutate(
    Category = case_when(
      str_detect(Category, "No") ~ "No",
      str_detect(Category, "Yes") ~ "Yes"
    ),
    Exposure = "Sexual Harassment",
    Outcome = "Depression"
  ) |> 
  select(-loc_3)



reg_dep_bul <- 
  
  bay_dep_bul_agg_adj |> 
  spread_draws(
    b_Intercept, b_work_33_dicYes, r_loc_3[loc_3, term]
    ) |> 
  mutate(
    region_log_No = b_Intercept + r_loc_3,
    region_log_Yes = b_Intercept + r_loc_3 + b_work_33_dicYes
    ) |>
  mutate(
    prob_dep_bul_No = plogis(region_log_No),
    prob_dep_bul_Yes = plogis(region_log_Yes)
    ) |> 
  group_by(loc_3) |> 
  summarise(
    No_bul_dep = mean(prob_dep_bul_No),
    Yes_bul_dep = mean(prob_dep_bul_Yes)
    ) |> 
  mutate(loc_3 = str_replace_all(loc_3, "\\.", " ")) |> 
  mutate(loc_3 = case_when(
           loc_3 == "М  Київ" ~ "М. Київ",
           TRUE ~ loc_3
         )) |> 
  add_row(tibble_row(loc_3 = "Ар Крим")) |> 
  left_join(ua_reg_conflict |> select(loc_3, loc_en, fips_code),
            by = "loc_3") |> 
  pivot_longer(
    cols = contains("bul_"),
    names_to = "Category",
    values_to = "Probability"
  ) |> 
  mutate(
    Category = case_when(
      str_detect(Category, "No") ~ "No",
      str_detect(Category, "Yes") ~ "Yes"
    ),
    Exposure = "Bullying",
    Outcome = "Depression"
  ) |> 
  select(-loc_3)



reg_dep_threats <- 
  
  bay_dep_threats_agg_adj |> 
  spread_draws(
    b_Intercept, b_work_31_dicYes, r_loc_3[loc_3, term]
    ) |> 
  mutate(
    region_log_No = b_Intercept + r_loc_3,
    region_log_Yes = b_Intercept + r_loc_3 + b_work_31_dicYes
    ) |>
  mutate(
    prob_dep_threats_No = plogis(region_log_No),
    prob_dep_threats_Yes = plogis(region_log_Yes)
    ) |> 
  group_by(loc_3) |> 
  summarise(
    No_threats_dep = mean(prob_dep_threats_No),
    Yes_threats_dep = mean(prob_dep_threats_Yes)
    ) |> 
  mutate(loc_3 = str_replace_all(loc_3, "\\.", " "))|> 
  mutate(loc_3 = case_when(
           loc_3 == "М  Київ" ~ "М. Київ",
           TRUE ~ loc_3
         )) |> 
  add_row(tibble_row(loc_3 = "Ар Крим")) |> 
  left_join(ua_reg_conflict |> select(loc_3, loc_en, fips_code),
            by = "loc_3") |> 
  pivot_longer(
    cols = contains("threats_"),
    names_to = "Category",
    values_to = "Probability"
  ) |> 
  mutate(
    Category = case_when(
      str_detect(Category, "No") ~ "No",
      str_detect(Category, "Yes") ~ "Yes"
    ),
    Exposure = "Violent threats",
    Outcome = "Depression"
  ) |> 
  select(-loc_3)




reg_dep_viol <- 
  
  bay_dep_viol_agg_adj |> 
  spread_draws(
    b_Intercept, b_work_32_dicYes, r_loc_3[loc_3, term]
    ) |> 
  mutate(
    region_log_No = b_Intercept + r_loc_3,
    region_log_Yes = b_Intercept + r_loc_3 + b_work_32_dicYes
    ) |>
  mutate(
    prob_dep_viol_No = plogis(region_log_No),
    prob_dep_viol_Yes = plogis(region_log_Yes)
    ) |> 
  group_by(loc_3) |> 
  summarise(
    No_viol_dep = mean(prob_dep_viol_No),
    Yes_viol_dep = mean(prob_dep_viol_Yes)
    ) |> 
  mutate(loc_3 = str_replace_all(loc_3, "\\.", " "))|> 
  mutate(loc_3 = case_when(
           loc_3 == "М  Київ" ~ "М. Київ",
           TRUE ~ loc_3
         )) |> 
  add_row(tibble_row(loc_3 = "Ар Крим")) |> 
  left_join(ua_reg_conflict |> select(loc_3, loc_en, fips_code),
            by = "loc_3") |> 
  pivot_longer(
    cols = contains("viol_"),
    names_to = "Category",
    values_to = "Probability"
  ) |> 
  mutate(
    Category = case_when(
      str_detect(Category, "No") ~ "No",
      str_detect(Category, "Yes") ~ "Yes"
    ),
    Exposure = "Physical violence",
    Outcome = "Depression"
  ) |> 
  select(-loc_3)




# join all
reg_dep_exp <- 
  
  reg_dep_har |> 
  select(loc_en, fips_code, Outcome, Exposure, Category, Probability) |> 
  bind_rows(reg_dep_bul, reg_dep_threats, reg_dep_viol)


reg_dep_exp

## # A tibble: 208 × 6
##    loc_en                 fips_code Outcome    Exposure     Category Probability
##    <fct>                  <chr>     <chr>      <chr>        <chr>          <dbl>
##  1 Ivano-Frankivsk Oblast UP06      Depression Sexual Hara… No             0.291
##  2 Ivano-Frankivsk Oblast UP06      Depression Sexual Hara… Yes            0.413
##  3 Volyn Oblast           UP24      Depression Sexual Hara… No             0.307
##  4 Volyn Oblast           UP24      Depression Sexual Hara… Yes            0.432
##  5 Vinnytsia Oblast       UP23      Depression Sexual Hara… No             0.316
##  6 Vinnytsia Oblast       UP23      Depression Sexual Hara… Yes            0.442
##  7 Dnipropetrovsk Oblast  UP04      Depression Sexual Hara… No             0.313
##  8 Dnipropetrovsk Oblast  UP04      Depression Sexual Hara… Yes            0.438
##  9 Donetsk Oblast         UP05      Depression Sexual Hara… No             0.306
## 10 Donetsk Oblast         UP05      Depression Sexual Hara… Yes            0.430
## # ℹ 198 more rows

saveRDS(reg_dep_exp, "out/dataframes/region_depression_assoc.rds")
writexl::write_xlsx(reg_dep_exp,
                    "out/dataframes/region_depression_assoc.xlsx")

Anxiety

bay_anx_har_agg_adj <-
  readRDS("out/bayesian_models_agg/bay_anx_har_agg_adj.rds")

bay_anx_bul_agg_adj <-
  readRDS("out/bayesian_models_agg/bay_anx_bul_agg_adj.rds")

bay_anx_threats_agg_adj <-
  readRDS("out/bayesian_models_agg/bay_anx_threats_agg_adj.rds")

bay_anx_viol_agg_adj <-
  readRDS("out/bayesian_models_agg/bay_anx_viol_agg_adj.rds")


# effect of violence on dep
# anxiety
reg_anx_har <- 
  
  bay_anx_har_agg_adj |> 
  spread_draws(
    b_Intercept, b_work_30_dicYes, r_loc_3[loc_3, term]
    ) |> 
  mutate(
    region_log_No = b_Intercept + r_loc_3,
    region_log_Yes = b_Intercept + r_loc_3 + b_work_30_dicYes
    ) |>
  mutate(
    prob_anx_har_No = plogis(region_log_No),
    prob_anx_har_Yes = plogis(region_log_Yes)
    ) |> 
  group_by(loc_3) |> 
  summarise(
    No_Har_anx = mean(prob_anx_har_No),
    Yes_Har_anx = mean(prob_anx_har_Yes)
    ) |> 
  mutate(loc_3 = str_replace_all(loc_3, "\\.", " "))|> 
  mutate(loc_3 = case_when(
           loc_3 == "М  Київ" ~ "М. Київ",
           TRUE ~ loc_3
         )) |> 
  add_row(tibble_row(loc_3 = "Ар Крим")) |> 
  left_join(ua_reg_conflict |> select(loc_3, loc_en, fips_code),
            by = "loc_3") |> 
  pivot_longer(
    cols = contains("Har_"),
    names_to = "Category",
    values_to = "Probability"
  ) |> 
  mutate(
    Category = case_when(
      str_detect(Category, "No") ~ "No",
      str_detect(Category, "Yes") ~ "Yes"
    ),
    Exposure = "Sexual Harassment",
    Outcome = "Anxiety"
  ) |> 
  select(-loc_3)



reg_anx_bul <- 
  
  bay_anx_bul_agg_adj |> 
  spread_draws(
    b_Intercept, b_work_33_dicYes, r_loc_3[loc_3, term]
    ) |> 
  mutate(
    region_log_No = b_Intercept + r_loc_3,
    region_log_Yes = b_Intercept + r_loc_3 + b_work_33_dicYes
    ) |>
  mutate(
    prob_anx_bul_No = plogis(region_log_No),
    prob_anx_bul_Yes = plogis(region_log_Yes)
    ) |> 
  group_by(loc_3) |> 
  summarise(
    No_bul_anx = mean(prob_anx_bul_No),
    Yes_bul_anx = mean(prob_anx_bul_Yes)
    ) |> 
  mutate(loc_3 = str_replace_all(loc_3, "\\.", " "))|> 
  mutate(loc_3 = case_when(
           loc_3 == "М  Київ" ~ "М. Київ",
           TRUE ~ loc_3
         )) |> 
  add_row(tibble_row(loc_3 = "Ар Крим")) |> 
  left_join(ua_reg_conflict |> select(loc_3, loc_en, fips_code),
            by = "loc_3") |> 
  pivot_longer(
    cols = contains("bul_"),
    names_to = "Category",
    values_to = "Probability"
  ) |> 
  mutate(
    Category = case_when(
      str_detect(Category, "No") ~ "No",
      str_detect(Category, "Yes") ~ "Yes"
    ),
    Exposure = "Bullying",
    Outcome = "Anxiety"
  ) |> 
  select(-loc_3)



reg_anx_threats <- 
  
  bay_anx_threats_agg_adj |> 
  spread_draws(
    b_Intercept, b_work_31_dicYes, r_loc_3[loc_3, term]
    ) |> 
  mutate(
    region_log_No = b_Intercept + r_loc_3,
    region_log_Yes = b_Intercept + r_loc_3 + b_work_31_dicYes
    ) |>
  mutate(
    prob_anx_threats_No = plogis(region_log_No),
    prob_anx_threats_Yes = plogis(region_log_Yes)
    ) |> 
  group_by(loc_3) |> 
  summarise(
    No_threats_anx = mean(prob_anx_threats_No),
    Yes_threats_anx = mean(prob_anx_threats_Yes)
    ) |> 
  mutate(loc_3 = str_replace_all(loc_3, "\\.", " "))|> 
  mutate(loc_3 = case_when(
           loc_3 == "М  Київ" ~ "М. Київ",
           TRUE ~ loc_3
         )) |> 
  add_row(tibble_row(loc_3 = "Ар Крим")) |> 
  left_join(ua_reg_conflict |> select(loc_3, loc_en, fips_code),
            by = "loc_3") |> 
  pivot_longer(
    cols = contains("threats_"),
    names_to = "Category",
    values_to = "Probability"
  ) |> 
  mutate(
    Category = case_when(
      str_detect(Category, "No") ~ "No",
      str_detect(Category, "Yes") ~ "Yes"
    ),
    Exposure = "Violent threats",
    Outcome = "Anxiety"
  ) |> 
  select(-loc_3)




reg_anx_viol <- 
  
  bay_anx_viol_agg_adj |> 
  spread_draws(
    b_Intercept, b_work_32_dicYes, r_loc_3[loc_3, term]
    ) |> 
  mutate(
    region_log_No = b_Intercept + r_loc_3,
    region_log_Yes = b_Intercept + r_loc_3 + b_work_32_dicYes
    ) |>
  mutate(
    prob_anx_viol_No = plogis(region_log_No),
    prob_anx_viol_Yes = plogis(region_log_Yes)
    ) |> 
  group_by(loc_3) |> 
  summarise(
    No_viol_anx = mean(prob_anx_viol_No),
    Yes_viol_anx = mean(prob_anx_viol_Yes)
    ) |> 
  mutate(loc_3 = str_replace_all(loc_3, "\\.", " ")) |> 
  mutate(loc_3 = case_when(
           loc_3 == "М  Київ" ~ "М. Київ",
           TRUE ~ loc_3
         )) |> 
  mutate(loc_3 = case_when(
           loc_3 == "М  Київ" ~ "М. Київ",
           TRUE ~ loc_3
         )) |> 
  add_row(tibble_row(loc_3 = "Ар Крим")) |> 
  left_join(ua_reg_conflict |> select(loc_3, loc_en, fips_code),
            by = "loc_3") |> 
  pivot_longer(
    cols = contains("viol_"),
    names_to = "Category",
    values_to = "Probability"
  ) |> 
  mutate(
    Category = case_when(
      str_detect(Category, "No") ~ "No",
      str_detect(Category, "Yes") ~ "Yes"
    ),
    Exposure = "Physical violence",
    Outcome = "Anxiety"
  ) |> 
  select(-loc_3)




# join all
reg_anx_exp <- 
  
  reg_anx_har |> 
  select(loc_en, fips_code, Outcome, Exposure, Category, Probability) |> 
  bind_rows(reg_anx_bul, reg_anx_threats, reg_anx_viol)



reg_anx_exp

## # A tibble: 208 × 6
##    loc_en                 fips_code Outcome Exposure        Category Probability
##    <fct>                  <chr>     <chr>   <chr>           <chr>          <dbl>
##  1 Ivano-Frankivsk Oblast UP06      Anxiety Sexual Harassm… No             0.235
##  2 Ivano-Frankivsk Oblast UP06      Anxiety Sexual Harassm… Yes            0.365
##  3 Volyn Oblast           UP24      Anxiety Sexual Harassm… No             0.229
##  4 Volyn Oblast           UP24      Anxiety Sexual Harassm… Yes            0.357
##  5 Vinnytsia Oblast       UP23      Anxiety Sexual Harassm… No             0.252
##  6 Vinnytsia Oblast       UP23      Anxiety Sexual Harassm… Yes            0.387
##  7 Dnipropetrovsk Oblast  UP04      Anxiety Sexual Harassm… No             0.256
##  8 Dnipropetrovsk Oblast  UP04      Anxiety Sexual Harassm… Yes            0.392
##  9 Donetsk Oblast         UP05      Anxiety Sexual Harassm… No             0.243
## 10 Donetsk Oblast         UP05      Anxiety Sexual Harassm… Yes            0.375
## # ℹ 198 more rows

saveRDS(reg_anx_exp, "out/dataframes/region_anxiety_assoc.rds")
writexl::write_xlsx(reg_anx_exp, "out/dataframes/region_anxiety_assoc.xlsx")

Maps

reg_dep_exp <- readRDS("out/dataframes/region_depression_assoc.rds")
reg_anx_exp <- readRDS("out/dataframes/region_anxiety_assoc.rds")


map_data <- 
  reg_dep_exp |>
  bind_rows(reg_anx_exp)


darkblue = "#0045A8"
aqua = "#38aec5"
cyan = "#00C4D6"

ukraine <- rnaturalearth::ne_states(country = "ukraine", 
                                  returnclass = "sf")

# crimea is included as russia (wrong)
crimea <- 
  rnaturalearth::ne_states(country = "russia",
                                   returnclass = "sf") |> 
  filter(name == "Crimea") |> 
  select(fips, geometry)

map_info_assoc <- 
  ukraine |>
  select(fips, geometry) |>   
  bind_rows(crimea) |> 
  rename(fips_code = fips) |>        
  left_join(map_data, by = "fips_code")


map_info_assoc

## Simple feature collection with 416 features and 6 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: 22.13284 ymin: 44.38105 xmax: 40.15954 ymax: 52.36895
## Geodetic CRS:  WGS 84
## First 10 features:
##    fips_code           loc_en    Outcome          Exposure Category Probability
## 1       UP02 Chernihiv Oblast Depression Sexual Harassment       No   0.3293017
## 2       UP02 Chernihiv Oblast Depression Sexual Harassment      Yes   0.4565794
## 3       UP02 Chernihiv Oblast Depression          Bullying       No   0.2708193
## 4       UP02 Chernihiv Oblast Depression          Bullying      Yes   0.4514300
## 5       UP02 Chernihiv Oblast Depression   Violent threats       No   0.3130299
## 6       UP02 Chernihiv Oblast Depression   Violent threats      Yes   0.4687180
## 7       UP02 Chernihiv Oblast Depression Physical violence       No   0.3530401
## 8       UP02 Chernihiv Oblast Depression Physical violence      Yes   0.4838148
## 9       UP02 Chernihiv Oblast    Anxiety Sexual Harassment       No   0.2625370
## 10      UP02 Chernihiv Oblast    Anxiety Sexual Harassment      Yes   0.3996444
##                          geometry
## 1  MULTIPOLYGON (((31.47485 52...
## 2  MULTIPOLYGON (((31.47485 52...
## 3  MULTIPOLYGON (((31.47485 52...
## 4  MULTIPOLYGON (((31.47485 52...
## 5  MULTIPOLYGON (((31.47485 52...
## 6  MULTIPOLYGON (((31.47485 52...
## 7  MULTIPOLYGON (((31.47485 52...
## 8  MULTIPOLYGON (((31.47485 52...
## 9  MULTIPOLYGON (((31.47485 52...
## 10 MULTIPOLYGON (((31.47485 52...

# depression

dep_assoc_map_grid <-
  
  map_info_assoc |> 
  filter(Outcome == "Depression") |> 
  mutate(Exposure = factor(Exposure, levels = exposure_order)) |>
  arrange(Exposure) |>
  ggplot() +
  geom_sf(aes(fill = Probability),
          color = "white",
          size = 0.2) +
  scale_fill_gradient(low = "#dae6f2",
                      high = darkblue,
                      na.value = "turquoise1",
                      labels = scales::percent,
                      limits = c(0.1, 0.6)) +
  geom_sf_text(
    aes(
      label = ifelse(
        is.na(Probability),
        "ND",
        scales::percent(Probability, accuracy = 1)
        ),
      color = Probability > 0.6
      ),
    size = 2.5,
    check_overlap = FALSE
    ) +
  coord_sf(expand = FALSE) + # removes internal padding between map and box
  scale_color_manual(values = c("black", "white"), guide = "none") +
  facet_grid(rows = vars(Exposure),
             cols = vars(Category)) +
  labs(fill = "Absolute risk") +
  ggpubr::theme_pubr() +
  theme(
    plot.margin = margin(t = 0, r = 0, b = 0, l = 0, unit = "cm"),
    panel.spacing = unit(0.2, "lines"),
    legend.position = "right",
    legend.box.margin = margin(0, 0, 0, 0),
    legend.margin = margin(0, 0, 0, 0),
    legend.text = element_text(size = 10),
    plot.title = element_text(hjust = 0.5),
    axis.title = element_blank(),
    axis.text = element_blank(),
    axis.ticks = element_blank(),
    axis.line = element_blank(),
    strip.text = element_text(size = 10, face = "bold")
    ) 


dep_assoc_map_grid

ggsave(
  "assoc_dep_map.pdf",
  plot = dep_assoc_map_grid,
  path = "out/",
  scale = 1.7,
  width = 210,
  height = 160,
  unit = "mm",
  dpi = 350
)

# anxiety


anx_assoc_map_grid <-
  map_info_assoc |> 
  filter(Outcome == "Anxiety") |> 
  mutate(Exposure = factor(Exposure, levels = exposure_order)) |>
  arrange(Exposure) |>
  ggplot() +
  geom_sf(aes(fill = Probability),
          color = "white",
          size = 0.2) +
  scale_fill_gradient(low = "#dae6f2",
                      high = darkblue,
                      na.value = "turquoise1",
                      labels = scales::percent,
                      limits = c(0.1, 0.6)) +
  geom_sf_text(
    aes(
      label = ifelse(
        is.na(Probability),
        "ND",
        scales::percent(Probability, accuracy = 1)
        )
      ),
    size = 2.5,
    check_overlap = FALSE
    ) +
  coord_sf(expand = FALSE) +
  scale_color_manual(values = c("black", "white"), guide = "none") +
  facet_grid(rows = vars(Exposure),
             cols = vars(Category)) +
  labs(
    fill = "Absolute risk") +
  ggpubr::theme_pubr() +
  theme(
    plot.margin = margin(t = 0, r = 0, b = 0, l = 0, unit = "cm"),
    panel.spacing = unit(0.2, "lines"),
    legend.position = "right",
    legend.box.margin = margin(0, 0, 0, 0),
    legend.margin = margin(0, 0, 0, 0),
    legend.text = element_text(size = 10),
    plot.title = element_text(hjust = 0.5),
    axis.title = element_blank(),
    axis.text = element_blank(),
    axis.ticks = element_blank(),
    axis.line = element_blank(),
    strip.text = element_text(size = 10, face = "bold")
    ) 
    
anx_assoc_map_grid

ggsave(
  "assoc_anx_map.pdf",
  plot = anx_assoc_map_grid,
  path = "out/",
  scale = 1.7,
  width = 210,
  height = 160,
  unit = "mm",
  dpi = 350
)

Cronbach’s Alpha

phq_items <- 
  ds_ua  |>  
  select(mh_phq_1, mh_phq_2, mh_phq_3, mh_phq_4, mh_phq_5, 
         mh_phq_6, mh_phq_7, mh_phq_8, mh_phq_9)

gad_items <- 
  ds_ua  |>  
  select(mh_gad_1, mh_gad_2, mh_gad_3, mh_gad_4, mh_gad_5, 
         mh_gad_6, mh_gad_7)

# Run

phq_alpha <- psych::alpha(phq_items)


gad_alpha <- psych::alpha(gad_items)


# table

reliability_table <- 
  tibble(
    Scale = c("PHQ-9", "GAD-7"),
    Items = c(9, 7),
    # We extract the Alpha once so we can use it in the calculations below
    Alpha = c(phq_alpha$total$raw_alpha, gad_alpha$total$raw_alpha),
    Lower_CI = Alpha - (1.96 * c(phq_alpha$total$ase, gad_alpha$total$ase)),
    Upper_CI = Alpha + (1.96 * c(phq_alpha$total$ase, gad_alpha$total$ase))
    ) |>
  mutate(across(where(is.numeric), \(x) round(x, 2))) |> 
  gt() |> 
  cols_label(
    Lower_CI = "Lower 95% CI",
    Upper_CI = "Upper 95% CI" 
  )

reliability_table

Scale	Items	Alpha	Lower 95% CI	Upper 95% CI
PHQ-9	9	0.89	0.89	0.89
GAD-7	7	0.92	0.92	0.92

gtsave(reliability_table, "out/tables/ukr_cronbach.docx")

Missing values

var_names_orig <- names(ds_ua)

names(ds_ua) <- paste0("Item ", seq_along(ds_ua))

var_names <-
  ds_ua |> 
  select(1:91) |> 
  select(-c("Item 1", 
            "Item 2", 
            "Item 3", 
            "Item 4",
            "Item 5",
            "Item 6",
            "Item 7",
            "Item 8",
            "Item 9",
            "Item 10",
            "Item 11",
            "Item 12",
            "Item 13",
            "Item 20",
            "Item 21",
            "Item 24",
            "Item 26",
            "Item 27",
            "Item 31",
            "Item 62")) |>
  colnames()
  

miss_through_plot <- 
  ds_ua |> 
  select(1:91) |> 
  select(-c("Item 1", 
            "Item 2", 
            "Item 3", 
            "Item 4",
            "Item 5",
            "Item 6",
            "Item 7",
            "Item 8",
            "Item 9",
            "Item 10",
            "Item 11",
            "Item 12",
            "Item 13",
            "Item 20",
            "Item 21",
            "Item 24",
            "Item 26",
            "Item 27",
            "Item 31",
            "Item 62")) |>  
  mutate(
    across(
      everything(),
      ~ as.character(.)
    )
  ) |>
  pivot_longer(
    everything(), 
    names_to = "variable", 
    values_to = "value"
    ) |>  
  mutate(missing = if_else(
    is.na(value), 
    "Missing", 
    "Present")) |> 
  count(variable, missing) |> 
  group_by(variable) |>
  mutate(prop = n / sum(n),
         variable = factor(variable, levels = var_names)) |> 
  filter(missing == "Missing") |>  
  ggplot(aes(x = variable, 
             y = prop)) +
  geom_col(position = "dodge") +
  scale_y_continuous(labels = scales::percent,
                     limits = c(0, 0.25)) +
  scale_fill_grey(start = 0.8, end = 0.2, name = "Data") +
  theme_minimal() +
  labs(x = "Variable", 
       y = "Percent") +
  theme(
    axis.text.x = element_text(angle = 45,
                               hjust = 1,
                               size = 10),
    axis.title.x = element_text(size = 10),
    axis.title.y = element_text(size = 10),
    axis.text.y = element_text(size = 10)
    )


miss_through_plot

ggsave(filename = "missing_vals_through.pdf",
       plot = miss_through_plot,
       path = "out/plots/",
       width = 210,
       height = 170,
       units = "mm",
       dpi = 320,
       scale = 1.5
       )


names(ds_ua) <- var_names_orig


rm(var_names, var_names_orig)